Nikolaos Koltsaklis

This paper proposes a three -stage coordination methodology to optimally manage congestion in day -ahead operation planning for Transmission Systems (TS) and Distribution Systems (DSs). The first stage provides the day -ahead model (DAM) results for the power units in the TS, and Energy Storage Systems (ESSs) and Electric Vehicles (EVs) in the DSs. The second stage applies the DAM results to the modelled TS to reveal any congestion issues. If congestion is depicted the need for congestion management (CM) by the Transmission System Operator (TSO) arises. Thus, the TSO performs an AC Optimal Power Flow (OPF) analysis, under power loss minimization, to provide the redispatch schedule in the TS and quantify the power transactions between TSO and the Distribution System Operators (DSOs). Finally, the DSOs examine if they can deliver these power transactions without congestion in their DSs, by applying AC OPF to minimize power losses. If this is not possible, then a relaxation scheme is applied to these power transactions in order to define the maximum DSO flexibility without congestion in the DS. The proposed methodology is applied to the simulated Greek TS and the results indicate that it can optimally redispatch the available assets with a coordinated interaction between TSO-DSO(s).

Smart homes are structural parts of the smart grid, since they contain controllable devices and energy management systems. In this work, we propose a reinforcement learning (RL)-based method for the energy scheduling of a smart home's energy storage system, heating ventilation and air conditioning system, and electric vehicle (EV). The proposed method targets to jointly minimize three evaluation metrics; the smart home's electricity cost, the residents' thermal discomfort, and the EV user's range anxiety. An advanced reinforcement learning algorithm, the twin delayed deep deterministic policy gradient (TD3), is utilized for this purpose together with a process, which is based on data clustering, that augments the similarity degree between the train and the test sets. As a result, the considered evaluation metrics show a significant improvement. The smart homes electricity cost, for instance, can be reduced by up to 11.2%, when compared with other RL-based works in the existing literature.

This work presents a model to co-optimize the energy and reserves markets, taking into account the penetration and participation of various flexibility providers in both markets. In particular, a detailed unit commitment model has been developed based on mixed-integer programming techniques incorporating energy storage systems with both charging and discharging options, electric vehicles with both grid-to-vehicle and vehicle-to-grid modes, and demand response programs for cost-optimal energy and ancillary services scheduling. The balancing services considered include Frequency Containment Reserves (FCR), automatic Frequency Restoration Reserves (aFRR), and manual Frequency Restoration Reserves (mFRR), in both upward and downward directions. The impact of all these flexibility providers on operational and economic aspects has been assessed through an illustrative case study of a power system with high penetration of renewable energy sources, including thermal and hydroelectric power units. The results highlight the superiority of results when considering the participation of all flexibility providers, especially in the ancillary services market, in terms of economic competitiveness, renewable energy curtailment, associated CO2 emissions, and utilization of costly energy resources. The growing share of flexibility providers in both energy management and reserve provision mix highlights the importance of these sources for power mixes with low carbon content. The methodological framework developed can be employed by system operators, market participants, and policymakers to provide price signals and optimize their resources and portfolios.

Extremely adaptable power systems are required, as the share of variable renewable energy sources increases. The variable renewable energy sources' ability to be installed on the grid, is frequently thought to be constrained by a limited flexible capacity. A general, methodological framework, for the optimal scheduling of an islanded power system, with a variety of flexibility resources, is presented in this work. In particular, it takes into account a significant amount of intermittent RES and the widespread use of electric vehicles that offer charging and discharging options. The modeling in this work also considers demand response programs' active market participation and the installation of energy storage capacities. Additionally, it covers the involvement of electricity interconnections, as a source of flexibility. Two illustrative case studies of an island power system connected to a mainland power system, have been used, to evaluate the applicability of the proposed strategy. The scheduling framework is daily, with an hourly interval. The results of the modeling show how important all of the flexibility resources are, for effective energy management and the supply of ancillary services, especially in cases for high-RES penetration. The proposed method can be used by market operators, policymakers and regulatory authorities, to choose the best system development, market design and portfolio synthesis.

Smart Home Energy Management Systems can manifest energy consumption reduction targets in the residential sector and can be viewed as an approach to transform the consumer into an active prosumer. The present paper presents a smart home energy management system that includes flexible appliances, electric vehicles, and energy storage units. Efficient forecasting algorithms support the robust operation of the smart home energy management system. Specifically, the smart home energy management system receives as inputs forecasts of demand, renewable energy sources including photovoltaics and Wind Turbine generations, and real-time prices. In order to minimize energy costs, a variety of algorithms is compared to provide highly accurate forecasts. (C) 2022 The Author(s). Published by Elsevier Ltd.

Smart homes that contain renewable energy sources, storage systems, and controllable loads will be key components of the future smart grid. In this article, we develop a reinforcement-learning (RL)-based scheme for the real-time energy management of a smart home that contains a photovoltaic system, a storage device, and a heating, ventilation, and air conditioning (HVAC) system. The objective of the proposed scheme is to minimize the smart home's electricity cost and the residents' thermal discomfort by appropriately scheduling the storage device and the HVAC system on a daily basis. The problem is formulated as a Markov decision process, which is solved using the deep deterministic policy gradient (DDPG) algorithm. The main contribution of our study compared to the existing literature on RL-based energy management is the development of a clustering process that partitions the training data set into more homogeneous training subsets. Different DDPG agents are trained based on the data included in the derived subsets, while in real time, the test days are assigned to the appropriate agent, which is able to achieve more efficient energy schedules when compared to a single DDPG agent that is trained based on a unified training data set.

This work presents an optimization framework for the optimal scheduling of a multi-energy microgrid based on Mixed-Integer Programming techniques and consisting of a number of aggregated end-users. With the objective of satisfying the microgrid's electricity and heat demands in a cost-optimal way, the microgrid includes several technologies such as micro combined heat and power units, gas turbines, heat pumps, renewable energy sources, exchanges with the main grid, as well as flexibility providers such as energy storage systems, electric vehicles, and demand response. One salient feature of the proposed framework is that it considers the contribution of power generating sources in the ancillary services provision, both up and down, providing an additional income for their operation and enhancing the grid operation. An illustrative case study has been used to test the applicability of the proposed approach in both economic and operational terms. The results underscore the significance of including the ancillary services market as a revenues source to the MES as well as the fact that the participation of various resources in both energy and ancillary services markets affects the operational scheduling of the microgrid, and the services provided by the flexibility providers play a major role in the overall cost reduction. System operators, aggregators, and market participants can utilize the proposed optimization framework to determine their operational and investment strategies for optimal resource utilization and portfolio selection. Copyright © 2021, AIDIC Servizi S.r.l.