Evaluation of load-following reserves for power systems with significant RES penetration considering risk management

In this study a novel two-stage stochastic programming based day-ahead joint energy and reserve scheduling model is developed. Demand-side as a reserve resource is explicitly modeled through responsive load aggregations, as well as large industrial consumers that directly participate in the scheduling procedure. Furthermore, a risk-hedging measure is introduced, namely the Conditional Value-at-Risk (CVaR), to analyze the behavior of energy and reserve scheduling by both the generation and the demand-side for a risk-averse ISO. The proposed methodology is tested on the practical non-interconnected insular power system of Crete, Greece, which is characterized by a significant penetration of Renewable Energy Sources (RES).Es la versión aceptada del documento. Se puede consultar la versión final en el DOI 10.1109/SEGE.2015.732457

Market-based TSO-DSO coordination for enhanced flexibility services provision

This paper proposes a framework for DSO-TSO market-based coordination in a feasible and transparent way, compatible with the European-type electricity market. The DSO participates in the balancing market as a Balance Responsible Party, by submitting price-taking offers that represent the net outcome of DSO scheduled actions at the distribution level. With this approach, the balancing market clearing results reflect the impact of scheduled actions at distribution level on system power balance. In addition, specific design aspects of the DSO market are highlighted that could align DSO and TSO market, increasing the market coordination efficiency and promoting the wider participation of distributed energy resources in both markets. The advantages of the proposed method are: (a) more accurate balancing market pricing signals while congestion management services and balancing services remain decoupled, (b) low data exchange requirements between the two Operators, (c) distributed energy resources opportunity to participate efficiently either in one or both markets, and finally (d) easy pan-European market-based coordination of TSOs-DSOs based on current balancing market development action plans. © 2022 Elsevier B.V

A multi-objective optimization approach to risk-constrained energy and reserve procurement using demand response

The large-scale integration of wind generation in power systems increases the need for reserve procurement in order to accommodate its highly uncertain nature, a fact that may overshadow its environmental and economic benefits. For this reason, the design of reserve procurement mechanisms should be reconsidered in order to embed resources that are capable of providing reserve services in an economically optimal way. In this study, a joint energy and reserve day-ahead market structure based on two-stage stochastic programming is presented. The developed model incorporates explicitly the participation of demand side resources in the provision of load following reserves. Since a load that incurs a demand reduction may need to recover this energy in other periods, different types of load recovery requirements are modeled. Furthermore, in order to evaluate the risk associated with the decisions of the system operator and to assess the effect of procuring and compensating load reductions, the Conditional Value-at-Risk metric is employed. In order to solve the resulting multi-objective optimization problem, a new approach based on an improved variant of the epsilon-constraint method is adopted. This study demonstrates that the proposed approach to risk management presents conceptual advantages over the commonly used weighted sum method

Optimal household appliances scheduling under day-ahead pricing and load-shaping demand response strategies

In this paper, a detailed home energy management system structure is developed to determine the optimal dayahead appliance scheduling of a smart household under hourly pricing and peak power-limiting (hard and soft power limitation)-based demand response strategies. All types of controllable assets have been explicitly modeled, including thermostatically controllable (air conditioners and water heaters) and nonthermostatically controllable (washing machines and dishwashers) appliances, together with electric vehicles (EVs). Furthermore, an energy storage system (ESS) and distributed generation at the end-user premises are taken into account. Bidirectional energy flow is also considered through advanced options for EV and ESS operation. Finally, a realistic test-case is presented with a sufficiently reduced time granularity being thoroughly discussed to investigate the effectiveness of the model. Stringent simulation results are provided using data gathered from real appliances and real measurements

A multi-objective optimization approach to risk-constrained energy and reserve procurement using demand response

The large-scale integration of wind generation in power systems increases the need for reserve procurement in order to accommodate its highly uncertain nature, a fact that may overshadow its environmental and economic benefits. For this reason, the design of reserve procurement mechanisms should be reconsidered in order to embed resources that are capable of providing reserve services in an economically optimal way. In this study, a joint energy and reserve day-ahead market structure based on two-stage stochastic programming is presented. The developed model incorporates explicitly the participation of demand side resources in the provision of load following reserves. Since a load that incurs a demand reduction may need to recover this energy in other periods, different types of load recovery requirements are modeled. Furthermore, in order to evaluate the risk associated with the decisions of the system operator and to assess the effect of procuring and compensating load reductions, the Conditional Value-at-Risk metric is employed. In order to solve the resulting multi-objective optimization problem, a new approach based on an improved variant of the epsilon-constraint method is adopted. This study demonstrates that the proposed approach to risk management presents conceptual advantages over the commonly used weighted sum method

Coordinated operation of a neighborhood of smart households comprising electric vehicles, energy storage and distributed generation

In this paper, the optimal operation of a neighborhood of smart households in terms of minimizing the total energy procurement cost is analyzed. Each household may comprise several assets such as electric vehicles, controllable appliances, energy storage and distributed generation. Bi-directional power flow is considered both at household and neighborhood level. Apart from the distributed generation unit, technological options such as vehicle-to-home and vehicle-to-grid are available to provide energy to cover self-consumption needs and to inject excessive energy back to the grid, respectively. The energy transactions are priced based on the net-metering principles considering a dynamic pricing tariff scheme. Furthermore, in order to prevent power peaks that could be harmful for the transformer, a limit is imposed to the total power that may be drawn by the households. Finally, in order to resolve potential competitive behavior, especially during relatively low price periods, a simple strategy in order to promote the fair usage of distribution transformer capacity is proposed

Demand response driven load pattern elasticity analysis for smart households

The recent interest in smart grid vision enables several smart applications in different parts of the power grid structure, where specific importance should be given to the demand side. As a result, changes in load patterns due to demand response (DR) activities at end-user premises, such as smart households, constitute a vital point to take into account both in system planning and operation phases. In this study, the assessment of the impacts of pricing based DR strategies on smart household load pattern variations is provided. The household load data sets are acquired from a provided model of a smart household, including appliance scheduling. Then, an artificial neural network (ANN) approach based on Wavelet Transform (WT) is employed for the forecasting of responsive residential load behaviors to different pricing schemes. From the literature perspective this study contributes by considering DR impacts on load pattern forecasting, being a very useful tool for market participants such as aggregators in future pool-based market structures, or for load serving entities to discuss potential change requirements in existing DR strategies, or even to effectively plan new ones

An EMD-ANN based prediction methodology for DR driven smart household load demand

This study proposes a model for the prediction of smart household load demand influenced by a dynamic pricing demand response (DR) program. Price-based DR programs have a considerable impact on household demand pattern due to the expected choice of customers or their home energy management systems (HEMSs) to use more energy in low price periods in order to reduce their electricity procurement cost. Many studies in the literature have dealt with power prediction, but the authors are prior in the field attempting to include the impact of different DR strategies on load demand prediction of smart households. The proposed methodology is expected to be valuable for utilities, retailers, aggregators, etc., in order to evaluate the success of their price-based DR strategies and predict adverse effects such as power peaks in normally off-peak periods and stress of infrastructure

Multi-objective reconfiguration of radial distribution systems using reliability indices

This paper deals with the distribution network reconfiguration problem in a multi-objective scope, aiming to determine the optimal radial configuration by means of minimizing the active power losses and a set of commonly used reliability indices formulated with reference to the number of customers. The indices are developed in a way consistent with a mixed-integer linear programming (MILP) approach. A key contribution of the paper is the efficient implementation of the [Formula: see text] -constraint method using lexicographic optimization in order to solve the multi-objective optimization problem. After the Pareto efficient solution set is generated, the resulting configurations are evaluated using a backward/forward sweep load-flow algorithm to verify that the solutions obtained are both non-dominated and feasible. Since the [Formula: see text] -constraint method generates the Pareto front but does not incorporate decision maker (DM) preferences, a multi-attribute decision making procedure, namely, the technique for order preference by similarity to ideal solution (TOPSIS) method, is used in order to rank the obtained solutions according to the DM preferences, facilitating the final selection. The applicability of the proposed method is assessed on a classical test system and on a practical distribution system