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Residential Demand Response Operation in a Microgrid

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Lecture by Prof. Pierluigi Siano.
18 March, 2016, 16:00 h. Universidad de Córdoba. Campus de Rabanales, Bldg. Leonardo Da Vinci

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Residential Demand Response Operation in a Microgrid

  1. 1. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 1 Residential Demand Response Operation in a Microgrid Pierluigi Siano Professor of Electrical Energy Engineering University of Salerno, Italy e-mail: psiano@unisa.it Short Course on Residential Demand Response Operation in a Microgrid Universidad de Córdoba. Campus de Rabanales, Bldg. Leonardo Da Vinci.
  2. 2. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 2 The University of Salerno The University of Salerno, one of the largest universities in Italy, this year was ranked as the first university in southern Italy. Its structure is that of a University Campus and its modern buildings offer many efficient services for teaching, research and student life in general such as laboratories, multimedia equipment, a language centre, libraries, a canteen, gyms and other sports facilities.
  3. 3. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 3 The University of Salerno
  4. 4. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 4 The University of Salerno
  5. 5. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 5 The University of Salerno
  6. 6. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 6 Outline Demand response: motivations, capabilities and key drivers Enabling Smart Technologies for Demand Response Energy Management Systems Results of a pilot Demand Response project in Italy Developing Demand Response research activities at University of Salerno Key challenges for Demand Response
  7. 7. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 7 Demand Response The objective of Demand Response (DR) is to make the load an active participant in balancing electricity supply and demand around the clock via side-by-side competition with supply-side resources DR allows loads curtailment/management in response to changes in the price of electricity over time, or to incentive payments designed to induce lower electricity use at times of high wholesale market prices or when system reliability is at risk1 1 http://ieeechicago.org/Portals/18/IEEE%20Chicago%20April%2013%20Newsletter%20FINAL.pdf
  8. 8. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 8 Demand Response Activities Strategic conservation Load shiftingValley filling Flexible load shape Peak clipping Strategic load growth
  9. 9. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 9 Demand Response implementation drivers The main drivers for Demand Response implementation are: Environmental concerns Reliability Smart grids technologies Advent of energy management service provider Policy incentives
  10. 10. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 10 Key Features of Smart Grids Smart grid applications increase the opportunities for Demand Response by providing real time data to producers and consumers Advanced metering solutions: to replace the legacy metering infrastructure Deployment of appropriate technologies, devices and services: to access and influence energy usage information in smart appliances and in the integration of renewable energy Combined digital intelligence and real-time communications: to improve the control of the transmission and distribution grids
  11. 11. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 11 Smart Grids
  12. 12. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 12 Active Networks Different conceptual models can be mentioned such as: Active Networks supported by ICT, Microgrids, Virtual Power Plants and an ‘Internet’ model - all of which could find applications, depending on geographical constraints and market evolution. Active Distribution Networks (ADNs) represent a possible development of “Smart Grid” concepts within distribution power systems. The active networks have been specifically identified as facilitators to offer connectivity and interaction capability for both DGs and customers.
  13. 13. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 13 Active Networks  In the initial stage, ANM will allow monitoring and remote control of the generation at the connection point to facilitate it integration in the system.  In the intermediate stage, ANM will permit the complete control system for all the distributed energy resources (DER) in a controlled area.
  14. 14. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 14 Active Networks Source: ADINE project The more advanced and emerging concept of AM is based on real-time monitoring and control of the grid. The AM scheme allows communication between coordinated voltage control and generator controls, loads and network devices, such as reactive compensators, voltage regulators, and on-load tap changers.
  15. 15. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 15 Microgrids They are low voltage networks with DG sources, together with local storage devices and controllable loads (e.g. water heaters and air conditioning). They have a total installed capacity in the range of between a few hundred kilowatts and a couple of megawatts. EMS
  16. 16. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 16 Microgrids – USA concept The Consortium for Electric Reliability Technology Solutions (CERTS) microgrid (CM) concept is one of the most world famous research project on microgrids. Its background is into the will to use the DERs to reduce the cost of electrical energy and improve the Power Quality Requirements principally considering the needs of industrial power plants. DERs are supervised by a centralized Energy Manager which maintains economic dispatch sending active power and voltage set-point to each Microsource Controller.
  17. 17. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 17 Microgrids –European concept The main differences with the US concept are in the attention here devoted to the market participation on which is based the optimal operation of the microgrid. The Figure shows a possible configuration of a microgrid and a general control scheme. The MGCC which is always responsible for the optimization of the Microgrid operation. Load Controllers are installed at the controllable loads to provide load control capabilities following demands from the MGCC, under a Demand Side Management policy or for load shedding. The hierarchical system control architecture comprises three critical control levels: • Local Micro Source Controllers (MC) and Load Controllers (LC) • MicroGrid System Central Controller (MGCC) • Distribution Management System (DMS). EMS
  18. 18. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 18 Virtual power plant DER units are too small and too numerous to be visible or manageable on an individual basis. Because of their size and multitude, distributed generators and responsive loads are currently not fully integrated into system operation and market-related activities. The concept of Virtual power plant (VPP) counteracts this problem by aggregating DER units into a portfolio that has similar characteristics to transmission connected generation today. A portfolio of smaller generators and demands. The concept is closely related to DER aggregation.
  19. 19. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 19 The ‘Internet’ model The vision of the internet model is: - “Every node in the electrical network of the future will be awake, responsive, adaptive, price-smart, eco-sensitive, real-time, flexible, humming - and interconnected with everything else” In the Internet model:  decision-making and control are distributed across nodes spread throughout the system  flows are bi-directional  the supplier of power for a given consumer vary from one time period to the next  the network use could vary as the network self-determines its configuration.
  20. 20. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 20 Enabling Smart Technologies for Demand Response Automated response technologies, enabling both enhanced and remote control of the energy consumption and peak load can be divided into three general categories: control devices, monitoring systems, communication systems.
  21. 21. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 21 Control Devices for Demand Response Load control devices are both stand-alone and integrated into an EMS for large facilities and consist of technologies such as: Load control switches are used for remote control of specific end use loads such as compressors or motors and are connected to the utility by means of communications systems. Smart thermostats are remotely controlled by the utility and/or the customer and allow the control of variations in temperatures’ settings with a softer control instead of using on-off switching devices.
  22. 22. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 22 Monitoring and Communications Systems for Demand Response Smart meter systems measure customer consumption in a certain time-interval and transmits measurements over a communication network to the utility or other actor responsible for metering. This information can be shared with end-use devices informing the customers about their energy consumption and related costs. Smart-meter types are distinguished according to the combination of some features such as the data-storage capability of the meter, the communication type (i.e. one-way or two-way), the connection with the energy supplier. The accuracy requirements of static billing meters are defined in IEC 61036 standards in order to preserve the accuracy of the measurement data. Smart meters generally exist within a broader infrastructure which is often called Advanced Metering Infrastructure (AMI).
  23. 23. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 23 Monitoring and Communications Systems for Demand Response Home Area Networks Smart Meters Neighborhood Area Networks Edge Routers (Collectors) Neighborhood Area Networks Meter Data Management System Utility Wide Area Network AMI System AMI denotes a system that, on request or on a pre-defined schedule, measures, saves and analyses energy usage, receiving information from devices such as electricity meters using various communication media. The smart grid communication architecture consisting of two- way communicating devices with the central SG controller, exhibits a hierarchical structure. An AMI network consists of a number of integrated technologies and applications including smart meters, wide-area networks (WANs), home area networks (HANs), meter data management systems (MDMS), operational gateways and systems for data integration into software application platforms, Neighborhood Area Networks (NANs).
  24. 24. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 24 Monitoring and Communications Systems for Demand Response Home Area Networks (HANs) allow connecting smart meters to controllable electrical devices and implement energy management functions by using devices such as programmable communicating thermostats and other load-control devices. Neighborhood Area Networks (NANs) are networks used for meter data collection. These data are transferred to a central database and used for various purposes. A Meter Data Management System (MDMS) is a database performing validation, editing and estimation on the AMI data in order to guarantee that the data are accurate and complete. It is also endowed with analytical tools that enable the cooperation with other information systems (operational gateways) thanks to which AMI can also support advanced management systems. The standard for the exchange of information of the distribution networks is based on CIM (Common Information Model) defines a control architecture that can deal with the complexity of smart grids and a bus of information, accessible to the different control functions, that can exchange the information related to the state of the system on the basis of a common format.
  25. 25. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 25 Monitoring and Communication Systems for Demand Response Networked Appliances Appliances can be designed for control via a network with access ports that connect to a communications bus sharing a common medium, as shown in Figure. A key component in any local area network is the network interface module (access port for remote control) contained in every device that uses the network. The interface converts internal device signals to a uniform format for the communications medium of the HAN. The technical elements for remote control for an appliance with energy mode control are:  a connection to a communication medium,  circuits to encode and decode the communication signals and embedded messages, plus a link to the appliance controller. Smart Grid Impact on Consumer Electronics Consumer Electronics Association (CEA), 2013
  26. 26. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 26 Monitoring and Communication Systems for Demand Response Both wireless and wired communication technology should accomplish to IEC 61850. Wireless communication technology can be either an option for HANs, NANs and WANs, or obligatory in case of Vehicle-to-Grid (V2G) communications, and various communication technology and standards could coexists in different part of the smart grid. IEEE 802.15.4 (ZigBee) and IEEE 802.11 (Wi-Fi) are appropriate technologies for smart meters in HANs and NANs, where the coverage range varies from tens to hundreds of meters. The coverage requirements (of tens of kilometers) for WANs impose the use of cellular wireless networks like GPRS, UMTS, LTE, or broadband wireless access networks like IEEE 802.16m (WiMax). Wired communication systems: depending on the desired coverage area, various technologies can be used for wired communication. Power Line Communications (PLCs) may be adopted for HANs and NANs in order to cover local/micro SG portions (up to hundreds of meters). Fiber optic communications may instead be implemented for WANs (tens of kilometers, and more).
  27. 27. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 27 Customer conceptual model in smart grids The ESI provides a secure interface for Utility-to- Consumer interactions. The ESI can act as a bridge to Building Automation System (BAS) or Energy Management System (EMS). The ESI serves as the information management gateway through which the customer domain interacts with energy management service providers. Basic functions of the ESI include demand response signaling (for example, communicating price information or critical peak period signals) as well as provision of customer energy usage information to residential energy management systems or in-home displays. The National Institute of Standards and Technology (NIST) elaborated the Framework and Roadmap for Smart Grid Interoperability Standards. It describes a high-level conceptual reference model for the Smart Grid. The boundaries of the Customer domain are typically considered to be the utility meter and the Energy Services Interface (ESI). NIST Framework and Roadmap for Smart Grid Interoperability
  28. 28. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 28 Energy Management Service Provider - Aggregator ESCOs offer commercial customers comprehensive energy usage analysis and recommendations for savings. They usually propose a financial arrangement to share in the savings, rather than just being paid for their advice. The EMSP is authorized to act as an intermediary between the Independent System Operator (ISO)/Regional Transmission Organization (RTO) and the users to deliver DR capabilities to meet ISO/RTO needs in its markets. Commercial service providers are also called Energy Service Companies (ESCOs) or Curtailment Service Providers (Aggregators). NIST Framework and Roadmap for Smart Grid Interoperability
  29. 29. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 29 Energy Management Service Provider - Aggregator Load Aggregators are energy management companies that offer to help utilities shed load in response to supply or distribution limitations. A Load Aggregator acts as intermediator between electricity end-users, who provides distributed energy resources, and those power system participants who wish to exploit these services. The aggregator's job is to enable the demand response and bring it to the wholesale market. This requires that the aggregator: 1) studies which customers can provide profitable demand response, 2) actively promotes the demand response service to customers, 3) installs control and communication devices at customer's premises and 4) provides financial incentives to the customers to provide demand response.
  30. 30. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 30 Energy Management Service Provider - Aggregator Who can be an aggregator? In the current liberalized regime, DSO’s cannot perform demand response aggregation because they cannot participate in electricity markets. Currently retailers are in the best position to become aggregators because they have connections to the electricity market and an existing relationship with the customers. The aggregator could also be a third party, a company who does not have any existing relationship with the customers as far as electricity business is considered. However, it could have a relationship in another field, such as facility management.
  31. 31. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 31 Energy Management Service Provider - Aggregator Customer’s remuneration Aggregators write contracts with commercial customers who are offered lower energy costs in exchange for occasional load shedding. They can arrange better energy prices for their customers by pooling loads. This offloads the marketing and management of load control from the utility. An availability fee is given for customers who make a contract with the aggregator and enable load control or control of other types of DER. The availability fee may be reduced by penalty payments if the customer does not follow the aggregator's control signals. An opposite to the availability fee is a rental payment for the control and communication equipment which the aggregator has installed. Payment can also be based explicitly on following the control calls (yes/no) or the power reduced due to control call in a demand response event. The customer's benefit can be based on dynamic tariffs provided by an aggregator retailer. The customer can be given a certain percentage of the aggregator's gross profit from selling DER to the market. A combination of the different payment components can be used to achieve a suitable risk and incentive level.
  32. 32. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 32 Energy Management Service Provider - Aggregator The remuneration, especially for the call payment, is closely connected to the way the customer’s resources are controlled. There are several ways to affect the customer behaviour to obtain DR. In case of small and medium-sized customers these can be divided into price-based options and direct load control.  Price-based control refers to changes in electricity use by customers in response to changes in the prices they pay (electricity tariff). In other words, the customer receives price information from his aggregator at specified intervals. The time resolution of the prices can be from several hours to less than one hour.  Volume-based control where the aggregator controls the total power drawn by a consumer, without regard to individual appliances.  In direct load control the aggregator can directly control the power drawn by one or more appliances at customer’s premises. This can take place automatically so that the aggregator can remote-control the appliances or so that he first notifies the customer who performs the actual control.
  33. 33. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 33 Energy Management Service Provider - Aggregator
  34. 34. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 34 Energy Management Systems for Demand Response In DR applications, Indirect Load Control is replacing Direct Load Control (where utility suppliers do operate customer appliances or devices remotely). Customers’ choices about using appliances are influenced by price or event messages and carried out with: manual action by consumers automatic actions by smart appliances decisions by an Energy Management Agent (EMA) that manages appliance operation. A system with EMA is called Distributed Load Control exploiting microprocessor based and combining Local and Direct Load Control with much increased flexibility and customer control. It is also possible to implement Distributed Load Control by sending utility prices and event notifications directly to smart appliances (Prices-to-Devices).
  35. 35. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 35 Energy Management Systems for Demand Response The EMA may switch energy sources from the public grid to local generators or a battery. The utility or service provider send price or event messages to all houses in real-time over a HAN such as the Internet. These signals enter the house through a residential gateway (Energy Service Interface) that also serves as a line of demarcation between utility and home owner equipment. Distributed Load Control with an Energy Management Agent (via Utility or Indipendent Service Providers)
  36. 36. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 36 Results of a pilot DR project in Italy
  37. 37. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 37 Results of a pilot DR project in Italy Decision Support and Energy Management System (DSEMS) Messages for Customers Loads Configuration Storage System Supply from the grid Supply local sources Enviromental Parameters UserRequirement Tariff Available Components ContractConstrain Messagesfrom DNO Energy Management System An Energy Management System has been designed and implemented that receives price and system signals and provides energy management of loads, air conditioning units, storages, local generation units according to user preferences. Outputs are the command signals used for the control of thermal and electrical loads and the messages for the end user. Pilot industrial research project “System for Energy Savings with Integrating of Air Conditioning” funded by the Italian Ministry of Economic Development and carried out with BTicino and other Italian Universities. Coordinator and Principal Investigator for the University of Salerno (2010-2014).
  38. 38. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 38 Results of a pilot DR project in Italy The EMS ensures the power supply and performs detachment or control of the electrical loads based on given priorities and according to different control functions that may be selected by the user. The Economy function determines during each period the best electrical load configuration to reduce the energy cost also considering user requirements and constraints and assuming a TOU tariff with different costs for “peak” and for “off-peak” hours. Shiftable loads (dishwasher, washing machine) are moved to the off-peak tariff period. The temperature set point of the air-conditioning units is controlled to reduce energy consumption:  during the peak tariff period  during the off-peak tariff period when the power consumption exceeds the available power (including local resources).
  39. 39. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 39 Results of a pilot DR project in Italy The Emergency function is automatically selected by the EMS after a failure in the distribution grid supply. In this case, the electrical supply is provided by a local generation (photovoltaic, micro-wind, micro-turbine, etc.) and by an electric energy storage system. The Energy function aims at assuring a given electrical energy consumption or economic expense in a prefixed period of time (according to the contract agreed with the supplier). The EMS sends messages to the user informing it about:  the daily-average consumption;  the allowed consumption to achieve the prefixed target. The Thermal Storage function changes the temperature set point of the air-conditioning in order to allow an anticipated cooling/heating in each controlled zone also on the basis of the local generation.
  40. 40. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 40 Results of a pilot DR project in Italy The Power function is selected so that the absorbed active power is limited by a prefixed threshold value and may allow user receiving an economic benefit from the DSO. The NET-Service function allows DSO controlling some selected electric loads in order to achieve benefits for the grid, while the end-user will receive a premium for the service it offers to the DSO. The Comfort function is selected when the user is willing to assure the maximum comfort in the house in terms both of indoor temperature and of electrical load usage. Controllable and shiftable loads are managed only to avoid that the maximum available active power is exceeded, thus improving the continuity of supply for the end-user.
  41. 41. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 41 Results of a pilot DR project in Italy TiDomus: mask for the selection of the electric loads. These control functions have been implemented in a simulation tool, named TiDomus that is able to reproduce different house environments by varying:  the type and the nominal power of the electric loads;  the thermal characteristics of the building;  the type and the technical characteristics of the air conditioning system;  the presence/absence of inhabitants in the house.
  42. 42. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 42 Results of a pilot DR project in Italy ESS Electric Source Simulator TBS Thermal Behaviour Simulator ELS Electric Load Simulator CLS Control Logic Simulator MAIN INPUTDATA OUTPUTDATA
  43. 43. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 43 Results of a pilot DR project in Italy Mask for the calculation of the primary energy requirements. Mask for the evaluation of the economic saving. TiDomus uses Monte Carlo Simulation for the extraction process of the daily power profile of the house starting from the knowledge of some social and economic factors.
  44. 44. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 44 Results of a pilot DR project in Italy The control functions have been coded with Stateflow of Matlab and then implemented on an ARM9 processor.
  45. 45. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 45 Results of a pilot DR project in Italy A new device (an IR interface) has been produced that is able to control the temperature set point of the air conditioning system by sending infrared commands. The interface is connected to the fieldbus and is therefore directly manageable by the EMS2 . 2 Applications made with SW OpenWebNetProtocol operating in various operating systems through appropriate gateway (SCS/SCS or USB/IP). SCS is an acronym for “Simplified Wiring System”. It uses a fieldbus network protocol and has applications in the field of home automation and building automation. It is used mainly in BTicino and Legrand installations.
  46. 46. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 46 Results of a pilot DR project in Italy Simulation tests have been performed. The system considers the following electrical loads: • Fixed loads; • Lights; • Dishwasher; • Washing Machine; • Dryer. The active absorbed power of dishwasher, washing machine and dryer (shiftable loads) is assumed to be constant in a cycle of work, while steady loads and lights have a fixed power consumption. The air-conditioning system is used both for summer cooling and for the winter heating. Its consumptions depends on the outdoor temperature and on the temperature set-point defined by the user.
  47. 47. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 47 Results of a pilot DR project in Italy The following inputs/outputs to the EMS have been considered: Inputs. • contractual power; • absorbed active power; • load on signal; • net availability; • tariff profile; • load priority list; • temperature set-point; • outdoor temperature. Outputs. • load control signals; • supply energy; • energy cost.
  48. 48. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 48 Results of a pilot DR project in Italy - Normal scenario In Fig. 3 is shown the outdoor temperature, the constant set-point temperature, TSet-Point (of 20 °C) and the indoor temperature, following TSet-Point. The power absorbed by the shiftable loads without considering the EMS are shown in in Fig.. Indoor, outdoor and Set-Point temperatures 0 4 8 12 16 20 24 15 20 25 30 35 40 Time [h] Degrees[C°] Tout Troom TSet-Point
  49. 49. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 49 Results of a pilot DR project in Italy Shiftable loads without control 0 4 8 12 16 20 24 0 0.5 1 1.5 2 Time [h] Power[kW] Steady Loads Lights Dishwasher Washer Dryer
  50. 50. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 50 Results of a pilot DR project in Italy Air-conditioning energy consumption without control 0 4 8 12 16 20 24 0 0.05 0.1 0.15 0.2 0.25 Energy[kWh] Time [h] Air-Conditioning Energy
  51. 51. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 51 Results of a pilot DR project in Italy Daily cost without control 0 4 8 12 16 20 24 0.02 0.04 0.06 0.08 0.1 0.12 0.14 Time [h] Cost[€] Energy Cost without control
  52. 52. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 52 Results of a pilot DR project in Italy Economy scenario In this scenario the EMS modifies the temperature set-point of the air-conditioning system in order to reduce the cost. As shown in Fig. , in the period from 8.00 to 19.00 (high tariff) the TSet-Point is increased up to 24 °C, while a constant temperature set-point of 22 °C has been set by the user during the day. As during the time period from 0.00 to 8.00 the windows are closed and there are some people inside the house, the air- conditioner works normally and the indoor temperature reaches the user set-point. On the other side, when there are no people inside the house and/or one window is opened, the air-conditioner switches off (see Fig.). Indoor and Set-Point temperatures 0 4 8 12 16 20 24 15 20 25 30 35 40 Time [h] Degrees[C°] Troom TSet Point
  53. 53. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 53 Results of a pilot DR project in Italy The way the EMS translates shiftable loads and turn on these loads in correspondence of a low price tariff period is shown in Fig. . In this case, the dishwasher and washing machine are shifted. Moreover, the system turn off the lights in absence of people in the home. In details, the system reduces the absorbed power by the lights of 20% after 15 minutes and turn off the lights after 30 minutes. Shiftable loads with control 0 4 8 12 16 20 24 0 0.5 1 1.5 2 Time [h] Power[kW] Lights Dishwasher Washer Dryer
  54. 54. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 54 Results of a pilot DR project in Italy The Figures show the energy consumption of the air-conditioning and the daily cost, respectively. It’s worth noting that the amount of energy consumption and the daily cost are lower with respect to the previous case without EMS. Air-conditioning energy consumption with control 0 4 8 12 16 20 24 0 0.05 0.1 0.15 0.2 0.25 Energy[kWh] Time [h] Air-Conditioning Energy
  55. 55. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 55 Results of a pilot DR project in Italy Daily cost with control 0 4 8 12 16 20 24 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 Time [h] Cost[€] Energy Cost with control
  56. 56. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 56 Results of a pilot DR project in Italy SUMMER DAY RESULTS Weekday Energy Consumption [kWh/day] Holiday Energy Consumption [kWh/day] Weekday Cost [€/day] Holiday Cost [€/day] With EMS 4.1 6.3 3.0 4.5 Without EMS 8.2 8.4 5.7 4.8
  57. 57. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 57 Results of a pilot DR project in Italy A comparison between the normal and economy scenario is obtained by using the MCS. Some results are shown in next figures in order to evidence the difference in terms of daily cost, considering a summer weekday without control and with control. Daily cost without control Daily cost with control 0 100 200 300 400 500 600 5 5.5 6 6.5 7 7.5 N simulations Cost[€/day] Energy Cost without control 0 100 200 300 400 500 600 2.6 2.8 3 3.2 3.4 3.6 3.8 4 4.2 N simulations Cost[€/day] Energy Cost with control
  58. 58. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 58 Results of a pilot DR project in Italy Daily cost without control Daily cost with control 5 5.5 6 6.5 7 7.5 0 10 20 30 40 50 60 70 80 90 Cost [€/day] Frequency 2.5 3 3.5 4 4.5 0 20 40 60 80 100 120 140 Cost [€/day] Frequency
  59. 59. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 59 Results of a pilot DR project in Italy SUMMER WEEKDAY RESULTS OBTAINED WITH MCS Energy Consumption [kWh/day] Cost [€/day] With EMS 4.0 3.2 Without EMS 10.4 6.1
  60. 60. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 60 Results of a pilot DR project in Italy Experimental tests have been performed on two real apartments and compared with simulation results in order to validate the models and the implemented functions. With the economy function, a mean percentage annual costs reduction in the range 5% - 10% depending on the efficiency class (from A to G) of the house can be evidenced. The first site, located in Cantù, near Como’s lake, is a cottage of 160 m2 (with energy performance class B). The installed electric power capacity is 6 kW with a 6 kW rated power PV system and a controlled air conditioning system. The Thermal Storage function exploits the excess electrical energy generated by the PV system for thermal storage by increasing the power consumption of the air conditioning systems for an anticipated cooling of the involved zones. Scenario Simulation Results Consumption [kWh/day] Experimental Results Consumption [kWh/day] Deviation [%] COMFORT 12.93 13.30 2.78% ECONOMY 9.58 9.23 3.79%
  61. 61. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 61 Results of a pilot DR project in Italy Cantù site with PV production. August 7, 2013. The programmed temperature profile is set every day at 26 °C, from 24.00 p.m. to 06.00 a.m., at 30 °C, from 06.00 a.m. to 18.00 p.m., and 26°C, from 18.00 p.m. to 24.00 a.m. The Thermal Storage is enabled during the whole day. With a change of 6 °C, the control system modifies the preset temperatures for each time slots. The activation of the “Thermal Storage” function occurs in the time period from 10 a.m. to 12 a.m., when the energy not consumed exceeds the set threshold. In the "zone 4 living" there is a change in the set-point (from 30 °C to 24 °C) and an increase in the energy consumption for the air conditioning.
  62. 62. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 62 Results of a pilot DR project in Italy References Int. Journals P. Siano, “Demand response and smart grids - A survey.” Renewable & Sustainable Energy Reviews, vol. 30, p. 461-478, 2014. P. Siano, G. Graditi, M. Atrigna, A. Piccolo, “Designing and testing decision support and energy management systems for smart homes”. Journal of Ambient Intelligence and Humanized Computing, Vol. 4, pp. 651- 661, 2013. P. Siano, G. Graditi, M.G. Ippolito, R. Lamedica, A. Piccolo, A. Ruvio, E. Santini, G. Zizzo, Innovative Control Logics for a Rational Utilization of Electric Loads and Air-Conditioning Systems in a Residential Building, Energy and Buildings, 102 (2015) 1–17 Int. Conferences P. Siano, G. Graditi M. Atrigna, A. Piccolo,“Energy management system for smart homes: Testing methodology and test-case generation”, 2013 International Conference on Clean Electrical Power (ICCEP 2013), pp. 766-771, 2013. P. Siano, M.G. Ippolito, G. Zizzo, A. Piccolo, “Definition and application of innovative control logics for residential energy optimization”, SPEEDAM 2014, Ischia, Italy, 18-20 June 2014. P. Siano, et alii, “Designing an Energy Management System for Smart Houses”, IEEE International Conference on Enviroment and Electrical Engineering, EEEIC 2015, Rome, 2015. Smart Grid Impact on Consumer Electronics, Consumer Electronics Association (CEA), 2013.
  63. 63. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 63 Developing DR research activities: a probabilistic methodology for evaluating the benefits of residential DR in a real time distribution energy market
  64. 64. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 64 Developing DR research activities: a probabilistic methodology for evaluating the benefits of residential DR in a real time distribution energy market The idea is that of considering real time nodal prices (D-LMPs) at the distribution level instead of a TOU tariff with different costs for “peak” and for “off-peak” hours that is, instead, based on transmission prices (without considering the distribution system constraints and power losses). The proposed approach introduces nodal prices3 at the distribution level in a distribution energy market (as in a microgrid). D-LMPs are based on three cost components (energy costs, congestions and power losses). In the developed probabilistic methodology the uncertainties related to the stochastic variations of the involved variables (load demand, user preferences, environmental conditions, house thermal behavior and wholesale market trends) are modeled by using Monte Carlo Simulation. 3 DSOs are in charge of purchasing high voltage energy from the wholesale market and transferring it to clients of distribution networks at a flat energy price generally calculated on the basis of the transmission nodal price, which can cause market inefficiencies because of the lack of consideration of the distribution system constraints.
  65. 65. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 65 A probabilistic methodology for evaluating the benefits of residential DR Transactive controllers are designed to control air conditioning units and some shiftable loads and to make bids on the distribution electricity market in response to D-LMPs and according end-user requirements. Temperature set-point and its maximum allowable variations are considered for the air conditioning. The desired operating period is taken into account for shiftable loads.
  66. 66. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 66 A probabilistic methodology for evaluating the benefits of residential DR System architecture market DR aggregator gateway controller house appliances DSO DGs offers bids Distribution network A DR aggregator, according to the signals received by the transactive controllers, makes the bids and gives feedback signals (bid acceptance or rejection).
  67. 67. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 67 Demand Response Economics Under inelastic demand (D1) extremely high price (P1) may result on a strained electricity market. If DR measures are employed the demand becomes more elastic (D2) and a much lower price will result in the market (P2). It is estimated that a 5% lowering of demand would result in a 50% price reduction during the peak hours of the California electricity crisis in 2000/2001.3 3 The Power to Choose - Enhancing Demand Response in Liberalised Electricity Markets Findings of IEA Demand Response Project, Presentation 2003 MCP MCQ
  68. 68. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 68 A probabilistic methodology for evaluating the benefits of residential DR Distribution acquisition market DR aggregators and DGs owners submit active power bids and offers to the DSO acquisition market in form of blocks for each time slot. The DSO carries out a RT intraday optimization every time slot (15 minutes). The market clearing quantity and prices (D-LMPs) at each bus are determined by maximizing the social welfare considering inter-temporal constraints as follows: 𝑀𝑎𝑥𝑖𝑚𝑖𝑧𝑒 𝑆𝑊( 𝐱, 𝐮) = ∑ 𝐵𝑗(𝑑𝑗 𝑁 𝑗 𝑗=1 ) − ∑ 𝐶ℎ(𝑔ℎ 𝑁ℎ ℎ=1 ) 0)( 0)( osubject t   gd,u,x,g gd,u,x,h
  69. 69. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 69 A probabilistic methodology for evaluating the benefits of residential DR For air conditioning units, the bid quantity is computed based on the required energy to achieve the desired indoor temperature. The bid price is computed on the basis of the mean of D-LMP at the bus in the previous 24 hours and on the indoor temperature distance from the set point. The air conditioning unit is switched on during the subsequent time slot only if the bid is accepted. A similar approach is adopted for shiftable loads whose bid prices are determined on the basis of a price forecast and of a prediction error on it. The bid price increases with time, also according to the appliance working time interval allowed by the user.
  70. 70. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 70 A probabilistic methodology for evaluating the benefits of residential DR Thermal loads management: HVAC algorithm
  71. 71. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 71 A probabilistic methodology for evaluating the benefits of residential DR Bidding curve (t)
  72. 72. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 72 A probabilistic methodology for evaluating the benefits of residential DR Shiftable loads algorithm
  73. 73. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 73 A probabilistic methodology for evaluating the benefits of residential DR Optimal interval in the float
  74. 74. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 74 A probabilistic methodology for evaluating the benefits of residential DR Blocks of the simulation model
  75. 75. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 75 A probabilistic methodology for evaluating the benefits of residential DR S/S - 2S/S - 1 49 4748 53 5051 52 5455 58 565762 596061 63 64 67 656671 68 69 70 72 75 737476 79 7778 83 80 8182 43 46 4544 30 353433 32 31 36 37 38 39 41 40 42 25 29282726 15 22 20 19181716 21 23 24 12 1311 14 2 3 4 5 6 7 8 1 10 9 A B C D E F G H I M DG DG DG L n n Legend Bus with dispatchable loads with DR Bus with fixed loads Diesel GeneratorDG Distribution Network used to test the model - 84-bus network 11.4-kV radial distribution system
  76. 76. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 76 A probabilistic methodology for evaluating the benefits of residential DR Simulation data Class of input Type of Input Data Network Feeders supply two 20 MVA, 33/11.4 kV transformers Feeder thermal limits between 150 A and 60 A Voltage limits ±10% of nominal value Buses’ loads 83 buses with fixed and dispatchable loads (each one with 120 residential loads) 29 buses with dispatchable loads and the remaining buses with non dispatchable loads Diesel generators (DGs) each of 660 kW, located at groups of 4 at buses 53, 69, and 83, and characterized by constant offer quantity equal to the size of the generators and a constant offer price equal to 160 euro/MWh (with takes into account both start-up, shutdown costs and operation costs) Houses A house has about 150 m2 useful floor area. Transmittances and thickness of walls, roof, floor, windows and doors make the energy efficiency class of the house being G as defined by EN 15217. In accordance to a usual practice for residential loads, power factors equal to 0.9 and constant in time have been assumed. External temperature (𝑇𝑒𝑥𝑡 𝑡 ) Time series have been collected for winter period in the south of Italy Thermal loads User’s air conditioning comfort setting At average, 200 C with an allowed variation of ± 20 C Shiftable loads Washing machines (𝑚 = 1) Rated power (𝑃𝑊𝑠ℎ𝑖𝑓𝑡1 ) of 2 kW and Operations Time (𝑂𝑇1) of 2 h. Dishwashers (𝑚 = 2) Rated power (𝑃𝑊𝑠ℎ𝑖𝑓𝑡2 ) of 2 kW and Operations Time (𝑂𝑇2) of 1.5 h.
  77. 77. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 77 A probabilistic methodology for evaluating the benefits of residential DR
  78. 78. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 78 A probabilistic methodology for evaluating the benefits of residential DR Average percentage of cost savings for a house considering all 29 dispatchable loads involved in the DR program (100% DR involvement) 0 5 10 15 20 25 30 35 40 0 8 16 24 32 Average percentage cost savings for the buses with DR [%] Percentagefrequency[%]
  79. 79. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 79 A probabilistic methodology for evaluating the benefits of residential DR Average percentage of cost savings for a house considering all dispatchable loads involved in the DR program 5% 15% 25% 35% 45% 55% 65% 75% 85% 5 8 13 27 29 37 39 43 44 45 46 50 52 53 54 55 58 61 62 63 66 68 72 74 76 78 80 81 83 Costsavingspercentage[%] Bus [identification number] 100% DR involvement 50% DR involvement 25% DR involvement feeder G feeder M feeder I Higher cost savings since without DR there are congestions on the lines 47-48 and M-77 Cost savings always higher than 10%
  80. 80. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 80 A probabilistic methodology for evaluating the benefits of residential DR Average percentage of cost savings for a house considering all dispatchable loads involved in the DR program 5% 15% 25% 35% 45% 55% 65% 75% 85% 5 8 13 27 29 37 39 43 44 45 46 50 52 53 54 55 58 61 62 63 66 68 72 74 76 78 80 81 83 Costsavingspercentage[%] Bus [identification number] 100% DR involvement 50% DR involvement 25% DR involvement feeder G feeder M feeder I The transactive controller of the air conditioning unit operates in such a way to decrease the temperature set point to its lower bound when the D-LMPs are high due to congestions and expensive electrical power from the DG. This is more frequent when the customers’ involvement is equal to 25% and causes higher average daily energy savings. Cost savings tends largely to increase with the reduction of costumers’ involvement in DR.
  81. 81. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 81 A probabilistic methodology for evaluating the benefits of residential DR Average percentage of cost savings for a house considering all dispatchable loads involved in the DR program 5% 15% 25% 35% 45% 55% 65% 75% 85% 5 8 13 27 29 37 39 43 44 45 46 50 52 53 54 55 58 61 62 63 66 68 72 74 76 78 80 81 83 Costsavingspercentage[%] Bus [identification number] 100% DR involvement 50% DR involvement 25% DR involvement feeder G feeder M feeder I Cost savings for a 100% of customers’ involvement are lower than those related to a 50% of customers’ involvement. Simultaneously displacement of many shiftable loads to hours characterized by a lower price forecast determines the peak rebound effect (due to the generation of electrical power from expensive DG during some few hours and consequent higher D-LMPs).
  82. 82. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 82 A probabilistic methodology for evaluating the benefits of residential DR Average percentage of cost savings for a house considering all dispatchable loads involved in the DR program 5% 15% 25% 35% 45% 55% 65% 75% 85% 5 8 13 27 29 37 39 43 44 45 46 50 52 53 54 55 58 61 62 63 66 68 72 74 76 78 80 81 83 Costsavingspercentage[%] Bus [identification number] 100% DR involvement 50% DR involvement 25% DR involvement feeder G feeder M feeder I Differently from what happens on other feeders, a percentage of 25% of customers’ involvement cannot generally alleviate congestions on feeder G. This implies lower cost savings for a 25% of customers’ involvement if compared to a 50% of customers’ involvement allowing alleviating congestions in most cases.
  83. 83. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 83 A probabilistic methodology for evaluating the benefits of residential DR Bus 52. D-LMP, active power and indoor temperature 50%DR (some relevant variables during a winter day considered in the MCS) 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 0 100 200 D-LMP [euro/MWh] DR WODR 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 0 0.05 0.1 Active Power[MW] 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 15 20 25 Time [h] Temperature[C]
  84. 84. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 84 A probabilistic methodology for evaluating the benefits of residential DR Bus 52. D-LMP, active power and indoor temperature 25%DR 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 0 100 200D-LMP [euro/MWh] DR WODR 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 0 0.05 0.1 Active Power[MW] 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 15 20 25 Time [h] Temperature[C]
  85. 85. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 85 A probabilistic methodology for evaluating the benefits of residential DR Bidding curve (t)
  86. 86. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 86 Discussion The method is able to guarantee a percentage of cost savings always higher than 10% both in the case without and with congestions on the distribution network. The transactive controllers generally allow reducing the peak of daily load curve. The adoption of D-LMPs in a RT electrical energy market can in most cases prevent congestions. This method enables a case by case detailed analysis that, as evidenced by the previous analysis, is in general required to evaluate cost savings (due to the complexity of interactions among transactive controllers, distribution network topology and technical constraints). References P. Siano, D. Sarno “Assessing the Benefits of Residential Demand Response in a Real Time Distribution Energy Market”, Applied Energy 161 (2016) 533–551. P. Siano et alii “A Novel Method for Evaluating the Impact of Residential Demand Response in a Real Time Distribution Energy Market” Journal of Ambient Intelligence and Humanized Computing, in press.
  87. 87. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 87 Key challenges for Demand Response
  88. 88. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 88 Demand Response regulatory and policy frameworks Potential benefits of Demand Response
  89. 89. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 89 Demand Response regulatory and policy frameworks - US Regulatory and policy frameworks, such as the Energy Policy Act of 2005, have been recently enacted that promote DR and allow customers and load aggregators taking part by means of DR resources in energy, capacity, and ancillary services markets. Also, the FERC (Federal Energy Regulatory Commission) Order 719 contributed to remove obstacles to the participation of DR in wholesale markets by allowing load aggregators bidding DR on behalf of retail customers into markets. In 2011, FERC Order 745, determined that DR resources should be compensated at the Locational Marginal Price (LMP) for their participation in wholesale markets, thus establishing an equal treatment between demand-side resources and generation. The order is highly controversial and has been opposed by a number of energy economists.
  90. 90. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 90 Demand Response regulatory and policy frameworks - Europe The existing EU regulatory framework makes DR possible, but its full potential will not be realized without further action from national policy-makers, regulators and energy companies, additional efforts should aim at:4 (i)Creating market-based and transparent incentives for DR that reward participation through dynamic prices without unnecessary constraints whilst respecting legal considerations on data security and protection, privacy, intrusion. (ii) Opening up the market to exploit the potential of DR, treating demand side resources fairly in relation to supply and elaborating clear and transparent market rules and technical requirements. (iii) Bringing the technology into the market through the roll-out of smart metering with the appropriate functionalities, creating the necessary framework for smart appliances and energy management systems. 4 European Commission Staff Working Document, Incorporing demand side flexibility, in particular demand response, in electricity markets, 2013.
  91. 91. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 91 Demand Response regulatory and policy frameworks - Europe Jessica Stromback, Demand Response: the value of non-use Smart Energy Demand Coalition, Smart Energy Demand Coalition, Berlin Energy Forum, Berlin, 10-11 February 2014 Demand side products and programmes are being created within the wholesale electricity market, with an increasing number of aggregators active in the markets (e.g. UK). Entry barriers to balancing and reserve markets are gradually being removed and time of use tariffs are available in several Member States for residential consumers (e.g. UK, FR, IT, ES). More comprehensive residential pricing and industrial load balancing programmes are being developed (e.g. FR).
  92. 92. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 92 Demand Response regulatory and policy frameworks in Italy In Italy, policy has focused mostly on efficiency, supported by tariffs related to peak loads (i.e., capacity) for many residential customers. Actually, demand side resources can participate in the day ahead market, but the interest has been low. Wholesale market operators can act as demand aggregators (dispatching user). However, there are no independent DR aggregators in Italy. (The bidder can decide to make a bid with an indication of price or to bid at 0 price). The interest in making bids with an indication of price in the day ahead market is currently low. 5 5 However, in 2012 there was an increase of 23% in the bids with indication of price, showing that consumers are willing to use more suitable pricing strategies, probably due to the effect that the economic crisis is having on the wholesale electricity market. In regard to the balancing market, the current requirements give access only to generation units and the regulatory framework for aggregated DR participation is under development. A capacity market administered by TERNA should be launched in 2017, but it will only be accessible by generation side resources. Participation in the balancing market would require an always operating control centre, which is a cost barrier for a new aggregator. The rules regarding verification and definition of baseline for demand side resources are not explicit yet.
  93. 93. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 93 Demand Response regulatory and policy frameworks in Italy Industrial loads participate via two “interruptible contracts” programs managed by the Italian TSO (TERNA): one for the mainland and one for Sicily and Sardinia. This program foresees a payment subject to a mechanism based on the number of interruptions called in the year.6 Italy has developed a wide interval smart meter (without an in-home display) rollout and has a long tradition in Time of Use programs for high & medium voltage consumers. Mandatory TOU tariffs have been introduced since July 2010 for the majority of customers who buy from the main supplier, ENEL (two time bands: one for “peak” hours and the other for “off-peak” hours). The regulator is running a major study in order to understand what impact this TOUP has on household consumption7 . (It is not necessarily popular: ENEL’s competitors advertise flat rates as an inducement to switch supplier.) 6 i.e. extra €/MW for each additional interruption if the number of interruptions is >10 or paid back if the number of interruptions is <10. In the case of Sardinia and Sicily extra €/MW for each additional interruption is paid if the number of interruptions is > 20. The total interruptible power contracted under the two mechanisms reached 4.318 MW in June 2012. The minimum bid limit is 1 MW. 7 RES caused an increase of pries during the previously considered “off-peak” hours thus making the TOUP not useful.
  94. 94. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 94 Potential benefits of Demand Response Operation Expansion Market Transmission and Distribution Relieve congestion Manage contingencies, avoiding outages Reduce overall losses Facilitate technical operation Defer investment in network reinforcement or increase long- term network reliability Generation Reduce energy generation in peak times: reduce cost of energy and possibly emissions Facilitate balance of supply and demand (especially important with intermittent generation) Reduce operating reserves requirements or increase short-term reliability of supply Avoid investment in peaking units Reduce capacity reserves requirements or increase long- term reliability of supply Allow more penetration of intermittent renewable sources Retailing Reduce risk of imbalances Reduce price volatility. New products, more consumer choice Demand Consumers more aware of cost and consumption, and even environmental impacts. Give consumers options to maximize their utility: opportunity to reduce electricity bills or receive payments Take investment decisions with greater awareness of consumption and cost Increase demand elasticity
  95. 95. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 95 Key challenges for Demand Response Need to establish reliable control strategies and market frameworks so that the DR resource can be optimized. Due to the lack of experience it is still needed to employ extensive assumptions when modelling and evaluating this resource. Reacting to high-prices, DR loads could all switch to the same low price-period, causing a peak rebound (which can be, in most cases, coped with RT transactions between customers and suppliers). If the DR is limited the system benefits may not be sufficient to cover the cost of the control and communications infrastructure for DSO. If differentials in real time prices vary over only a small range, the savings for consumers may not be sufficient to induce investments in DR programs (as consumers may not be able to recoup their costs of installation or justify the burden of responding to prices).
  96. 96. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 96 Key challenges for Demand Response Electric utilities need ruling to allow consumers and consumer electronics companies using any means and devices to manage energy, as long as they do not harm the grid. Consumers should not need the approval of the public utility before buying an energy management product from a consumer electronics company. Likewise, consumers should be free to contract with third-party energy management service providers without approval of the utility.
  97. 97. SHORT COURSE ON RESIDENTIAL DEMAND RESPONSE OPERATION IN A MICROGRID PIERLUIGI SIANO 97 Additional References on Demand Response P. Siano, “Demand response and smart grids - A survey.” Renewable & Sustainable Energy Reviews, vol. 30, p. 461-478, 2014. Zakariazadeh A, Homaee O, Jadid S, P. Siano. A new approach for real time voltage control using demand response in an automated distribution system. Appl Energy Elsevier2014;114:157–66. Zakariazadeh A, Jadid S, P. Siano. Stochastic operational scheduling of smart distribution system considering wind generation and demand response programs. Int J Electr Power Energy SystElsevier2014;63:218–25. Zakariazadeh A, Jadid S, P. Siano. Multi-objective scheduling of electric vehicles in smart distribution system. Energy Convers Manage Elsevier 2014;79:43–53. Zakariazadeh A, Jadid S, P. Siano. Economic-environmental energy and reserve scheduling of smart distribution system: A multiobjective mathematical programming approach. Energy Convers Manage Elsevier 2014;78:151–64. Zakariazadeh A, Jadid S, P. Siano. Stochastic multi-objective operational planning of smart distribution systems considering demand response programs. Electr Power Syst Res Elsevier 2014;111:156–68. Mazidi M, Zakariazadeh A, Jadid S, P. Siano. Integrated scheduling of renewable generation and demand responseprograms in a microgrid Energy Convers Manage Elsevier 2014;86:1118–26. Zakariazadeh A, Jadid S, P. Siano. Smart microgrid energy and reserve scheduling with demand response using stochastic optimization. Int J Electr Power Energy Syst Elsevier 2014;63:523–33. C. Cecati, C. Citro, P. Siano, (2011). “Combined Operations of Renewable Energy Systems and Responsive Demand in a Smart Grid”, IEEE Transactions on Sustainable Energy. Vol. 2 (4). pp. 468-476. Shafie-khah, M., Heydarian-Forushani E., Golshan M.E.H., P. Siano., Moghaddam, M.P., Sheikh-El-Eslami, M.K., Catalão, J.P.S., Optimal trading of plug-in electric vehicle aggregation agents in a market environment for sustainability, Applied Energy, Vol. 162, 2016, pp. 601-612.

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