Local uses of photovoltaic (PV) energy within neighborhood PV prosumers become more economical than the individual operation of prosumers. In the present work, the hourly optimized total cost of energy sharing of peer-to-peer (P2P) PV prosumers for a microgrid is proposed. Initially, a dynamic internal pricing model is prepared for the energy sharing operation. Furthermore, considering the adjustable load of prosumers, an equiponderant cost model is formulated concerning economic costs and user interest. Finally, the formulated cost model is transformed into an optimization problem and is solved using the krill herd algorithm to get the ultimate optimized hourly total cost of energy sharing. This optimized cost provides the maximum economic profit to all the participating PV prosumers in the microgrid.

1. 10th National Power Electronics Conference
15th to 17th Dec 2021
IIT Bhubaneswar
PRESENTER: PLABON SAHA
PAPER ID: 36
1
Hourly Energy Sharing Model of
Peer-to-Peer PV Prosumers for
Microgrids with Price-based Demand
Response
Authors: Plabon Saha, Surya Sen and Parthasarathi Bera

2. 10th National Power Electronics Conference -2021 ( NPEC 2021)
Introduction
2
❑ Electricity Market.
❑ Medium-term planning horizon.
❑ Competitive Electricity Market.
❑ Risk Management.
❑ PV Prosumers.
❑ Energy Sharing Formation.
❑ Local uses of PV energy within neighborhood PV prosumers become more
economical than the individual operation of prosumers.

3. 10th National Power Electronics Conference -2021 ( NPEC 2021)
Energy Sharing Formation of Microgrid
3
Fig.1. Energy sharing formation of MG.
❑ Multiple PV prosumers take part in the energy
sharing zone of a microgrid.
❑ Every PV prosumer consists of a U-EMS, PV
system, load, meters, inverter, etc.
❑ ESA (energy service agent).
❑ ESA needs to charge service fees to the
prosumers.
❑ U-EMS (user energy management system).
❑ Self-consumption is the first priority among all PV
prosumers.

4. 10th National Power Electronics Conference -2021 ( NPEC 2021)
Internal Pricing Model
4
Fig.2. Pricing model between utility grid, ESA and PV prosumers.

5. 10th National Power Electronics Conference -2021 ( NPEC 2021)
Basic Data for Simulation Cases
5
Fig.3. Single-line diagram of the proposed microgrid. Fig.4. Generation data of PV prosumers.
Fig.5. Load demand data of PV prosumers. Fig..6 Net energy data of PV prosumers.

6. 10th National Power Electronics Conference -2021 ( NPEC 2021)
Problem Formulation
❑ According to the basic principle of economics, price and supply-demand ratio (SDR) are
inversely proportional to each other.
SDRh
=
TSPh
TBPh
❑ In general, price is inversely proportional to SDR. Internal selling-price is represented as:
Psell
h
= f SDRh = ൞
Upsell∙Upbuy
SDRh∙ Upbuy−Upsell +Upsell , 0 ≤ SDRh
≤ 1
Upsell
, SDRh
> 1
❑ The internal buying-price is represented as:
Pbuy
h
= f SDRh = ൝
SDRh ∙ Psell
h
+ Upbuy ∙ 1 − SDRh , 0 ≤ SDRh ≤ 1
Upsell, SDRh > 1
6

7. 10th National Power Electronics Conference -2021 ( NPEC 2021)
Implementation
❑ The main focusing area of this work is the hourly energy sharing of microgrid within all the
PV prosumers. So that the equation of optimize calculative total cost at a particular hour h is
defined as:
minAPCh
APh
, Ph
=
෍
x=1
n
(Px
h
APx
h
− GPx
h
+ λx(APx
h
− CPx
h
)2
)
s. t. σx=1
n
APx
h
= σx=1
n
CPx
h
min CPx ≤ APx
h ≤ max(CPx)
GPx
h−APx
h ≤ Bm
❑ Considering all the uncertainties a optimization problem is formulate and it transform into a
solvable optimization problem.
❑ Using the Krill Herd optimization algorithm, this optimization problem has been solved by
Matlab R2019b software.
7

8. 10th National Power Electronics Conference -2021 ( NPEC 2021)
Optimization Result
Time
(hour)
Total Net Energy
(kWh)
Total Cost
(CYN)
1 89.51 130.85
2 97.8 142.97
3 109.09 159.47
4 121.24 177.24
5 139.52 203.96
6 145.16 212.20
7 122.01 178.36
8 80.43 38.00
9 17.8 8.29
10 -18.93 -8.81
11 -45.66 -21.26
12 -65.55 -30.53
13 -74.63 -34.76
14 -60.27 -28.07
15 -33.06 -15.39
16 3.17 1.47
17 55.33 26.05
18 92.87 135.76
19 107.81 157.60
20 102.3 149.55
21 93.64 136.89
22 86.82 126.92
23 84.24 123.15
24 82.9 121.19
8
Fig.7. Total load and total net energy curve.
Fig.8. Optimized total cost in energy sharing zone at 16:00 hr.

9. 10th National Power Electronics Conference -2021 ( NPEC 2021)
Conclusion
This work presents the energy sharing of peer-to-peer PV prosumers inside a microgrid.
Considering the willingness of load shifting, one internal pricing model and an internal
cost model of PV prosumers have been prepared. Internal prices between all the
prosumers are the joint decision of them. Finally, an optimization problem has been
formulated and solved by using Krill Herd algorithm and the total cost of energy sharing
on an hourly basis has been optimized. This proposed method can comfortably implement
in the energy sharing of PV prosumers in the microgrid. In this research, realistic data has
been used and shown that by using this proposed method, for each hour, all PV prosumers
can save the cost together and can achieve the maximum profit throughout the day.
9

10. 10th National Power Electronics Conference -2021 ( NPEC 2021)
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