Multi-Area Active Distribution Network Scheduling in the Presence of Soft Open Points based on Information Gap Decision Theory

In this paper, a three-level framework is proposed to determine the optimal scheduling of a Multi-Area Active Distribution Network in the presence of inter-area Soft Open Points (SOPs). In this framework, the uncertainty of renewable generation and forecasted demand is modeled using information gap decision theory in a risk-averse manner. Coordinated scheduling of Controllable Distributed Generators (CDGs) and SOPs, inter-area energy exchanges and energy trading with upstream network considering the uncertainties are the contribution of the presented method. To improve the computational efficiency and to achieve the optimal solution, the scheduling problem is modeled as a second-order conic programming in which the operational and security constraints of the network, CDG limitations, and operational constraints of SOPs are accurately modeled and the problem is solved by executing CPLEX solver in MATLAB environment. A case study on IEEE 33-bus test system showcases the superiority of the proposed model compared to meta-heuristic algorithms such as particle swarm optimization, genetic algorithm, and gravitational search algorithm

Interactive Multi-level planning for energy management in clustered microgrids considering flexible demands

This paper presents a novel interactive multi-level planning strategy for the energy management of distribution networks with clustered microgrids (CMGs). CMGs are a group of microgrids with multiple renewable energy resources that comprise various technologies, such as photovoltaic systems, wind turbines, micro turbines and electric vehicles. This study develops an innovative multi-level optimization framework for the energy management coordination between microgrids and CMGs in the lower level, between clusters and distribution systems, and finally between distribution systems and upstream networks in the upper level. Accordingly, an hourly optimal energy management (HOEM) system is applied to minimize the multi-objective objective function for each level. The lower level may be operated in islanded or grid-connected mode in some hours. This is decided by changing switches between MGs, clusters, and grids, while the upper level is only operated in the grid-connected mode. Moreover, a demand response program that has a great effect on the hourly planning of switches is modeled in the upper level. The proposed model is tested on CMGs and actual distribution systems. The results show the significance of this planning strategy in the techno-economic aspects and optimal power transaction in the distribution system operation.© 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Reinforcement Learning Applied to Multi Agent Modelling, the Case of the Iranian Power Market

With increasing competition in the wholesale Electricity markets and advances in behavioral economics in recent years, the multi-agent modeling approach has been applied widely to simulate the outcome of the markets. The electricity market consists of power generating agents that compete over production in daily auction conducted by an independent system operator (ISO). The market clearing mechanism can be seen as a static game that repeats every hour. In this game, an agent proposes her price for the next day and the ISO chooses the best proposals that minimizes the total costs given the demand and the technical constraints. Agents are also assumed to learn from the outcomes and adjust their biding strategy accordingly. In this paper, we develop an agent-based model for the day-ahead and pay-as-bid electricity market in Iran. The objective is to compare the outcome of the market measured by the agents profit and the time to converge using three different strategies: greedy, random and reinforcement learning. The simulation results indicate that the reinforcement learning leads to higher profits with a faster convergence rate than the other two strategies