A Real-Time Price (RTP) based Automatic Demand Response (ADR) strategy for PV-assisted Electric Vehicle (EV) Charging Station (PVCS) without Vehicle to Grid (V2G) is proposed. The charging process is modeled as a Dynamic Linear Program (DLP) instead of the normal day-ahead and real-time regulation strategy, to capture the advantages of both global and real-time optimization. Different from conventional price forecasting algorithms, a Dynamic Price Vector Formation Model (DPVFM) is proposed based on a clustering algorithm to form a RTP vector for a particular day. A Dynamic Feasible Energy Demand Region (DFEDR) model considering grid voltage profiles is designed to calculate the lower and upper bounds. A deduction method is proposed to deal with the unknown information of future intervals, such as the actual stochastic arrival and departure times of EVs, which make the DFEDR model suitable for global optimization. Finally, both the comparative cases articulate the advantages of the developed methods and the validity in reducing electricity costs, mitigating peak charging demand, and improving PV self-consumption of the proposed strategy are verified through simulation scenarios.

Alternating-current (AC) power flow (PF) presents difficulties in power system analysis and optimization due to its nonlinearity. Progress has been made to approximately linearize the AC PF over the last few decades. However, few studies have reported the simultaneous accurate approximation of reactive power and transmission losses. To bridge this gap, this paper investigates the linear approximation of AC PF considering the accuracy of the reactive load flows and transmission losses. Using the logarithmic transform of voltage magnitudes, a linear PF (LPF) model involving tap changers and phase shifters is derived from the approximation analysis of general branch flows. Transmission power loss and loss-concerned complex branch flow are also formulated. Cold-start and warm-start LPF calculation methods associated with injection compensation are also developed. Numerical simulations are performed to compare the proposed models and several state-of-the-art LPF models using 25 practical-scale test systems. The simulation results demonstrate the advantages of the proposed model compared to the others in approximating the voltage magnitudes, branch flows and power losses. The effectiveness of proper compensation injection in improving the solution accuracy is also verified.

With increasing deployment of distributed energy resources, the energy market which aims for local generation and load profile redistribution is facing the challenge to accommodate various types of participants. To realize social welfare maximization with privacy preserving in a dynamic energy market, this article propose a multiagent reinforcement learning (MARL) method for quotation decision optimization in continuous double auction (CDA)-based peer-to-peer (P2P) energy market. To address the nonstationarity and privacy violation brought by multiagent context, we utilize mean-field approximation to abstract the unauthorized local information of other agents from the public market dynamics. An abstract Q-value function is developed for each agent to infer the neighbor agents' local observation and action through the public clearing results in the dynamic CDA market. Moreover, to avoid sparse reward so as to stabilize the learning process, we propose a dynamic potential-based reward shaping term in the reward. Without altering the learnt optimal policies, the agents can be informed with the additional energy storage state as the reward shaping in each time instants. To validate the effectiveness and economy of our proposed method, simulation studies are conducted on a real-world dataset. Simulation results show that the proposed MARL method produces up to 17% more convergent episodic reward and 67% less energy bills which indicates competitive convergence performance and significant economic benefits.

Coordination efficiency between different energy sectors with privacy preservation has become a technical bottleneck in distributed operation of the integrated electricity and heat systems (IEHS). This article investigates, for the first time, an equivalent model-based non-iterative solution for this issue. An equivalent model of district heating networks (DHNs) retaining the temperature quasi-dynamics is derived. Based on the equivalence, all the state variables in DHNs are explicitly expressed by the heat power generation, and the feasible region of DHNs is projected on the coupling boundaries of the IEHS. The existence of this equivalent model is proved under a mild sufficient condition. The application of the proposed equivalent model is demonstrated via the distributed unit commitment for IEHS. Case studies of two test systems validate the effectiveness and the efficiency of the solution for distributed IEHS operation.