The Planning Method of the Multi-Energy Cloud Management Platform with Key Technologies and P2P Trade of Prosumers
Abstract
:1. Introduction
1.1. Motivation
- To establish the platform’s business mode, such as the functions and trade mode;
- To overcome the technological barriers, including the communication construction of monitoring system, information interaction among the automated information systems, increasing the distributed resources’ common information model (CIM) and interface data splicing in EMS;
- Research on the current trade mode suitable for China by combining some positive elements of P2P and trade pattern under the regulation influenced by the main grid to promote the transition of market revolution.
1.2. Paper Innovation
- (1)
- The traditional research on the platform focuses on the distributed coordination control method and application scenarios of which most conclusions are prospective and theory framework. To help to realize it in the real-world, we proposed the key communication and information technology base on the demonstration project;
- (2)
- For feasibility and methodology, we simulated typical multi-energy prosumers’ self-sufficiency optimization process and P2P trade for understanding the benefits and impact of P2P to consider reasonable regulation. The relationship in the real-world, the energy market stakeholders, such as prosumers, integrated energy service company, grid company, platform manager, and regulatory are discussed for their roles in the platform.
1.3. Structure of the Article
2. Literature Review
3. Planning and Design of Cloud Platform
3.1. Participants of Cloud Platform
3.2. The Five-Layer Framework of Information Interconnection
3.3. The Communication Function Architecture
3.4. Modeling and Optimization
3.4.1. Modeling
- (1)
- The objective function F can be positive for payout or negative for benefit. If the EV is assumed to be uncontrolled without the controlled costs, the cost F is the sum of the interruptible load cost CIL,t, transferable decreased load cost CTL-D,t, micro gas turbine’s operation cost CMT,t, energy sharing buy cost Cbuy,t, storage charge and discharge power cost CESS,t, minus the energy sharing sell cost Csell,t, minus wind power subsidy CWG,t, and minus PV subsidy CPV,t. The various costs are equal to the price pIL,t, pTL-D,t, pMT,t, pbuy,t, pESS,t, psell,t, pWG,t, pPV,t multiplied by 1 h of corresponding power;
- (2)
- The prosumers can sell excess power to the main grid at pgrid, while the other peers sell through P2P at price psell,t after self-sufficiency. If the peers are rational to maximize benefit, power will be bought from the main grid when pgrid ≤ psell,t, while P2P trade will be succeed when pgrid ≥ psell,t. Thus, ToU tariff is very important and useful information for both sides of P2P trade because it decides whether consumers buy power directly from the main grid or other peers through P2P trade [38]. Furthermore, the users also have irrational behavior, increasing the randomness of quotation. Thus, in this paper, the quoted price pbuy,t and psell,t are assumed to be uniform distribution U(a, b);
- (3)
- The CCHP provides electricity whether it provides heat or not due to its two operation modes, often combined with the electric heat pump’s heating and cooling ability. The heating and cooling can be self-balanced among a CCHP system because the heat and cool load is fixed. The electricity, heat and cooling energies are simultaneously, only electricity can be used for P2P trade considering limitations of the transmission and loss [38]. Thus, we can predict the demand for electricity, cooling and heat, but P2P only for power at present. Thus, the heat and cooling balance constraints are neglected;
- (4)
- The power balance equality constraint is the balance of output and input power, including the micro gas turbine’s active power PMT,t, wind power generation PWG,t, PV power generation PPV,t, energy sharing buy power Pbuy,t, energy sharing sell power Psell,t, interruptible load power PIL,t, transferable load decrease power PTL-D,t, transferable load increase power PTL-I,t, prosumer’s load PLoad,t, EV load PEV,t, electrochemical energy storage charge power Pcharge,t and discharge power Pdischarge,t;
- (5)
- The energy sharing buy power Pbuy,t and sell power Psell,t are limited by the line transmitted exchange power limit Pexchangelimit and energy sharing buy state kt and sell state (1-kt), kt = 0 or 1;
- (6)
- The micro gas turbine’s active power PMT,t constraint is limited by the maximum output PMTmax and minimum output PMTmin;
- (7)
- The wind PWG,t and PV PPV,t output constraint is the predicted value PWGforecast,t and PPVforecast,t considering the small positive and negative errors as ±∆ε and ±∆δ, respectively. The stochastic optimization method can model and resolve it. If ignoring the forecast errors, ∆ε≈0 and ∆δ≈0, the predicted wind and PV power are the determined values. In this paper, the prediction errors are neglected;
- (8)
- The interruptible load is limited by the maximum power limitation PILmax;
- (9)
- The transferable decrease load is limited by the peak load ceiling PTL-Dpeak, if the PLoad,t > PTL-Dpeak during the peak load periods, we transfer the excess load PTL-D,t1 to the valley load periods PTL-I,t2 from 0:00 am to 6:00 am;
- (10)
- If considering the EV uncontrolled, the EV load constraint is the predicted value PEVforecast,t with the forecast errors ±∆ϕ;
- (11)
- The Pcharge,t and Pdischarge,t are limited by the maximum charge power Pchargemax and maximum discharge power Pdischargemax, respectively;
- (12)
- For the prosumer with PV-WG-ESS, the overall output characteristic is a horizontal line after self-balancing. When it is subsidized, the cost is a constant at the maximum output.
3.4.2. Optimization Algorithm Flow Chart and Pseudo-Code
3.5. P2P Distributed Trading Mode
- (1)
- Step 1: To release information.
- (2)
- Step 2: Flexible bilateral consultation and trading.
- (3)
- Step 3: Check and adjust the settlement for the deviation power quantity and deal with the unbalanced power and unsuccessfully traded users.
4. Key Technology
4.1. Construction of Monitoring System
4.2. Interaction between Cloud Platform and Other Systems
4.3. Distributed Resource Model and Interface Data Splicing of EMS
5. P2P Trading Simulation Based on MATLAB
5.1. Parameter Setting
5.2. The Program Steps
5.2.1. The Program Steps of P2P Distributed Trade
- (1)
- Step 1: Initialization. Input the basic parameters of generation, load type, and 10 integrated energy users;
- (2)
- Step 2: The optimization solution of 10 integrated energy users. According to the basic buying price from and selling price to the superior power grid, the linear optimization problem for 10 users with the objective of minimum each user’s cost is solved under the equality constraint of power self-balance and inequality constraints of operating limitations. To find out the potential users who need to buy and sell power to the superior power grid;
- (3)
- Step 3: Bidding stage. Organize the users with generation feature to sell power, and that with load features to buy power with 24 hours’ different slightly fluctuating prices. The sellers declare the selling power and price. The buyers declare the buying power and price. The quoted price can be higher or lower than the basic exchanging price to the superior power grid, to create incentives for the integrated energy users.
- (4)
- Step 4: Finish the transaction and settlement. The successful transaction is settled according to the average price of the seller and the buyer. The failed transaction is purchased at the determined price by power grid company;
- (5)
- Step 5: Benefits analysis. To be decentralized, avoid the power grid charging an intermediate fee among these integrated energy users through the price difference.
5.2.2. Bidding Influenced by Grid’s ToU
5.3. Simulation Results
5.3.1. The Optimized Operation Scenarios of 10 Prosumers
5.3.2. P2P Distributed Trading
5.3.3. Aggregator Scenario of Prosumer 4 and 8
5.3.4. Bidding Strategy Analysis
5.3.5. Benefit and Behavior Feasibility Analysis
6. Conclusions
- (1)
- For feasibility, although currently there are no policies or rules legally allowing P2P energy trade, it must be validated in theory and practice so that the policy makers can understand the benefits and impact of P2P trade to consider reasonable regulation. Additionally, the power market hasn’t yet real-time pricing, but the ToU price selling to and buying from grid can be used as a bidding reference. P2P trade carries uncertain risks, but as a whole, it splits the grid’s revenue among prosumers to reduce government’s pressure on subsidies. Due to the risks of P2P trade, the platform managers must have a high level of credit, and the strict supervision is necessary to ensure high levels of reliability and security;
- (2)
- For methodology, this paper presents detailed modeling, algorithm, matching rule and pseudo-code for optimization. For the case verification, this paper simulates the electricity trade scenarios among different prosumers with different characteristics of industrial, commercial and residential load, distributed generation, energy storage, and micro gas turbine of CCHP. In addition, for the relationship in the real-world, the energy market stakeholders, such as prosumers, integrated energy service company, grid company, platform manager, and regulatory are discussed for their roles in the platform;
- (3)
- For technology, the information interconnection system is built according to standard five-layer structure. The distributed resource modeling and interface data splicing of EMS are important in managing the distributed energy equipment ledger. The communication function architecture needs to achieve some basic functions, including the real-time monitoring, and control management. Other functions, such as the devices’ state assessment and the remote application based on the handhold APP, can be further developed. The construction of platform needs the comprehensive technology of the communication and information technology, software and electrical engineering to realize the development of the basic and extended functions.
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Parameters | Values |
---|---|
Power limitation/(MW) | 0.3 |
Capacity limitation/(MW) | 1.5 |
Charging coefficient | 0.9 |
Discharging coefficient | 0.9 |
Scheduling cost/(CNY/kWh) | 0.5 |
Parameters | Values |
---|---|
Minimum start up time/ Minimum shutdown time/(h) | 2/2 |
Cost for start up or shutdown/(CNY) | 20 |
Generation cost/(CNY/kWh) | 0.5 |
Upper limit of active power output/Lower limit of that/(MW) | 0.3/0.1 |
Unit ramp rate for start up or shutdown/(MW/h) | 0.3 |
Parameters | p.u. | Reason for Setting |
---|---|---|
ToU distribution power price | 1 | Base 24 h price |
Wind subsidy | 0.2 | Proposed to 0.2 of the ToU distribution power price. |
PV subsidy | 0.22 | Proposed to 0.22 of the ToU distribution power price. |
ToU selling to grid (ɑmin,t) | 0.8 | “ToU distribution power price × (1 − 20%)” according to local rule. |
ToU buying from grid (ɑmax,t) | 1.2 | “ToU distribution power price × (1 + 20%)” according to local rule. |
Wind-PV-storage combined price | 0.4 | Proposed to 0.4 of the ToU distribution power price. |
Bidding price ([ɑmin,t, ɑmax,t]) | [0.8, 1.2] | “ToU distribution power price × (1 ± 20%)”. |
User Side | Load Type | Wind Power | PV Power | Micro Gas Turbine | Interruptible Load | Translation Load | EV | Electrical Storage |
---|---|---|---|---|---|---|---|---|
Prosumer 1 | Residential | √ | √ | × | √ | √ | √ | × |
Prosumer 2 | Residential +Commercial | √ | √ | √ | √ | × | × | × |
Prosumer 3 | Residential | × | √ | √ | × | × | × | × |
Prosumer 4 | Residential | √ | √ | × | × | × | × | × |
Prosumer 5 | Commercial | √ | √ | × | × | × | × | × |
Prosumer 6 | Industrial | √ | √ | √ | × | × | × | √ |
Prosumer 7 | Residential + Commercial | √ | × | √ | × | × | × | × |
Prosumer 8 | Having no load | √ | √ | √ | × | × | × | × |
Prosumer 9 | Residential + Commercial + Industrial | √ | √ | √ | × | × | × | × |
Prosumer 10 | Residential + Commercial | √ | √ | √ | × | × | × | √ |
Parameters | Values | |
---|---|---|
Interruptible load | Power limitation/(MW) | [0, 0.1] |
Price of cost/(CNY/kWh) | 0.5 | |
Translation load | Peak load more than which needing transferred/(MW) | 1.25 |
Price of cost/(CNY/kWh) | 0.3 | |
EV | Power limitation/(MW) | 0.2 |
Price of scheduling cost/(CNY/kWh) | 0.45 |
Scenario 1: P2P Distributed Trade (CNY) | Scenario 2: Exchanged with Grid (CNY) | |
---|---|---|
Prosumer 1 | 290.9 | 721.2 |
Prosumer 2 | −719.0 | −562.1 |
Prosumer 3 | 391.3 | 932.0 |
Prosumer 4 | −6211.1 | −4945.0 |
Prosumer 5 | 869.4 | 1424.9 |
Prosumer 6 | 6148.7 | 7414.4 |
Prosumer 7 | 3257.5 | 3937.3 |
Prosumer 8 | −6476.4 | −5177.1 |
Prosumer 9 | 152.1 | 447.8 |
Prosumer 10 | 2296.4 | 2791.8 |
Total cost of 10 users | 0.0 | 6985.2 |
Bid for 1:00–2:00 | Buyer | Seller | ||||
Buyer | Buying Price (CNY/MWh) | Buying Power (MW) | Seller | Selling Price (CNY/MWh) | Selling Power (MW) | |
Prosumer 1 | 361.52 | 0.7300 | Aggregator 4 | 322.12 | −0.8400 | |
Prosumer 2 | 399.32 | 0.1350 | Prosumer 5 | 302.64 | −0.5000 | |
Prosumer 3 | 352.48 | 0.4350 | Prosumer 6 | 304.74 | −0.1721 | |
Prosumer 10 | 424.88 | 0.3700 | Prosumer 7 | 364.43 | −0.2325 | |
Prosumer 9 | 332.05 | −0.1795 |
Bid for 1:00–2:00 | Buyer | Seller | Matching Average Price (CNY/MWh) | Matched Power (MW) | Cost (CNY) | Real-Time Price of Grid (CNY/MWh) |
10 | 5 | 363.76 | 0.3700 | 134.6 | Sell to grid: 292.64, Buy from grid: 438.96 | |
2 | 5 | 350.98 | 0.1300 | 45.6 | ||
2 | 6 | 352.03 | 0.0050 | 1.8 | ||
1 | 6 | 333.13 | 0.1671 | 55.7 | ||
1 | 4 | 341.83 | 0.5629 | 192.4 | ||
3 | 4 | 337.30 | 0.2771 | 93.5 | ||
3 | 9 | 342.27 | 0.1579 | 54.0 |
Scenario 3: P2P Distributed Bidding (CNY) | Scenario 4: Exchanged with Grid (CNY) | |
---|---|---|
Prosumer 1 | −402.7 | 117.4 |
Prosumer 2 | −692.8 | −448.4 |
Prosumer 3 | 496.2 | 901.5 |
Aggregator 4 | −9488.3 | −7452.9 |
Prosumer 5 | 1535.8 | 1989.2 |
Prosumer 6 | 6206.2 | 7759.1 |
Prosumer 7 | 3472.4 | 4185.6 |
Prosumer 9 | −747.4 | −536.7 |
Prosumer 10 | −379.5 | 207.3 |
Total cost of 10 users | 0.0 | 6722.2 |
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Li, J.; Xing, Y.; Huang, X.; Zhang, D. The Planning Method of the Multi-Energy Cloud Management Platform with Key Technologies and P2P Trade of Prosumers. Processes 2022, 10, 2272. https://doi.org/10.3390/pr10112272
Li J, Xing Y, Huang X, Zhang D. The Planning Method of the Multi-Energy Cloud Management Platform with Key Technologies and P2P Trade of Prosumers. Processes. 2022; 10(11):2272. https://doi.org/10.3390/pr10112272
Chicago/Turabian StyleLi, Junfang, Yue Xing, Xuejin Huang, and Donghui Zhang. 2022. "The Planning Method of the Multi-Energy Cloud Management Platform with Key Technologies and P2P Trade of Prosumers" Processes 10, no. 11: 2272. https://doi.org/10.3390/pr10112272
APA StyleLi, J., Xing, Y., Huang, X., & Zhang, D. (2022). The Planning Method of the Multi-Energy Cloud Management Platform with Key Technologies and P2P Trade of Prosumers. Processes, 10(11), 2272. https://doi.org/10.3390/pr10112272