Decentralized and Collaborative Scheduling Approach for Active Distribution Network with Multiple Virtual Power Plants
Abstract
:1. Introduction
1.1. Motivation
1.2. Literature Survey
1.3. Contributions
- (1)
- At present, the application of SADMM in power system optimal operation is few. Aiming at solving distributed day ahead scheduling problem, the SADMM was used in the distributed active distribution system with multiple VPPs. On the premise of autonomous energy management of ADN and VPPs, decoupling the optimization models of virtual power plants and distribution networks without the participation of central coordinators was realized to achieve fully decentralized optimization. Compared with the traditional centralized optimization methods, the method proposed in this paper has good convergence performance. It can achieve the scheduling independence of each agent, protect the data privacy of each agent, and is more suitable for the environment of the power market.
- (2)
- This paper integrated distributed energy and large-scale EVs in distribution network through VPP cluster, and adopts two-stage stochastic optimization method including pre-scheduling stage and re-scheduling stage to deal with the stochastic uncertainty of distributed wind and light outputs, so as to realize the collaborative complementarity and overall optimization of the whole distribution system. Compared with the independent optimization mode of distributed energy and EV and the traditional deterministic scheduling method without considering the uncertainty of wind and light, the model in this paper can make full use of the advanced coordinated control technology of VPP to aggregate the distributed energy and EV groups. Furthermore, it can make full use of the mobile energy storage characteristics of EVs, then the adverse effects of disorderly charging and discharging of EVs and uncertainties of distributed wind and light power outputs on dispatching operation of active distribution network were alleviated.
- (3)
- The simulation results showed that the proposed decentralized optimization method based on SADMM has good convergence performance, it can converge to almost the same running cost as centralized optimization by 16 iterations. When the discharge loss cost of power battery is high, VPP will not reduce the operation cost because of V2G reverse discharge of EV. However, under the incentive of electricity price policy, the EV charging load during peak load period is transferred to the low load valley to charge, which effectively reduces the operation cost and peak–valley load difference. The proposed two-stage stochastic optimization method can ensure that the day-ahead scheduling plan can be transferred to various error conditions smoothly. Although the operation cost has increased, it can effectively deal with the uncertainty of distributed scenery.
1.4. Organization
2. Scheduling Model of Active Distribution System with Multi Virtual Power Plants
2.1. Active Distribution Network Scheduling Model
2.1.1. Objective Function
2.1.2. Constraints
2.2. Two-Stage Stochastic Schedule Model for Virtual Power Plant
2.2.1. Objective Function
2.2.2. Constraint Conditions in Prescheduling Phase
2.2.3. Constraints of Rescheduling Phase
2.2.4. Boundary Coupling Characteristics between Virtual Power Plant and Active Distribution Network
3. Distributed Collaborative Model Based on SADMM
3.1. Basic Principles of Standard ADMM Algorithm
3.2. The Basic Principle of SADMM Algorithm
3.3. The Solving Process
- (1)
- Set the iteration number = 1, initialize the algorithm parameters of SADMM.
- (2)
- Independently solve the ADN and VPP economic dispatch models in a decentralized manner.
- (3)
- To judge whether Equation (32) is satisfied, if yes, the iteration ends, or continue the next step.
- (4)
- , update the tie line interaction power according to the Equations (30) and (31), and turn to step (2).
4. Example Analysis
4.1. Basic Data
4.2. Result Analysis
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Scheduling Methods | Prescheduling Cost/$ | Rescheduling Cost/$ | Total Cost/$ |
---|---|---|---|
Deterministic day ahead scheduling | 2118 | 0 | 2118 |
Two-stage stochastic scheduling | 2079 | 282 | 2361 |
Case | Model | Algorithm | Total Generation Cost ($) | Iterations | Calculation Time (s) |
---|---|---|---|---|---|
1 | Deterministic | Centralized | 2287 | 1 | 3.5 |
2 | Two-stage stochastic | Centralized | 2303 | 1 | 20.6 |
3 | Deterministic | Decentralized | 2336 | 14 | 102.7 |
4 | Two-stage stochastic | Decentralized | 2361 | 16 | 192.4 |
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Li, X.; Zhao, D.; Guo, B. Decentralized and Collaborative Scheduling Approach for Active Distribution Network with Multiple Virtual Power Plants. Energies 2018, 11, 3208. https://doi.org/10.3390/en11113208
Li X, Zhao D, Guo B. Decentralized and Collaborative Scheduling Approach for Active Distribution Network with Multiple Virtual Power Plants. Energies. 2018; 11(11):3208. https://doi.org/10.3390/en11113208
Chicago/Turabian StyleLi, Xiangyu, Dongmei Zhao, and Baicang Guo. 2018. "Decentralized and Collaborative Scheduling Approach for Active Distribution Network with Multiple Virtual Power Plants" Energies 11, no. 11: 3208. https://doi.org/10.3390/en11113208