Wind power with energy storage arbitrage in day-ahead market by a stochastic MILP approach

This paper is about a support information management system for a wind power (WP) producer having an energy storage system (ESS) and participating in a day-ahead electricity market. Energy storage can play not only a leading role in mitigation of the effect of uncertainty faced by a WP producer, but also allow for conversion of wind energy into electric energy to be stored and then released at favourable hours. This storage provides capability for arbitrage, allowing an increase on profit of a WP producer, but must be supported by a convenient problem formulation. The formulation proposed for the support information management system is based on an approach of stochasticity written as a mixed integer linear programming problem. WP and market prices are considered as stochastic processes represented by a set of scenarios. The charging/discharging of the ESS are considered dependent on scenarios of market prices and on scenarios of WP. The effectiveness of the proposed formulation is tested by comparison of case studies using data from the Iberian Electricity Market. The comparison is in favour of the proposed consideration of stochasticity

Aggregators' Optimal Bidding Strategy in Sequential Day-Ahead and Intraday Electricity Spot Markets

This paper proposes a probabilistic optimization method that produces optimal bidding curves to be submitted by an aggregator to the day-ahead electricity market and the intraday market, considering the flexible demand of his customers (based in time dependent resources such as batteries and shiftable demand) and taking into account the possible imbalance costs as well as the uncertainty of forecasts (market prices, demand, and renewable energy sources (RES) generation). The optimization strategy aims to minimize the total cost of the traded energy over a whole day, taking into account the intertemporal constraints. The proposed formulation leads to the solution of different linear optimization problems, following the natural temporal sequence of electricity spot markets. Intertemporal constraints regarding time dependent resources are fulfilled through a scheduling process performed after the day-ahead market clearing. Each of the different problems is of moderate dimension and requires short computation times. The benefits of the proposed strategy are assessed comparing the payments done by an aggregator over a sample period of one year following different deterministic and probabilistic strategies. Results show that probabilistic strategy reports better benefits for aggregators participating in power markets.This work was supported by the European research project IDE4L (Ref. FP7-SMARTCITIES-2013-608860) and the Spanish project RESmart (Ref. ENE2013-48690-C2-1-R)

Benefits of cross-border citizen energy communities at distribution system level

One challenge of the EU energy transition is the integration of renewable electricity generation in the distribution system. EU energy law proposes a possible solution by introducing “citizen energy communities” (Directive 2019/944/EU) which may be open for “cross-border participation”. This article proposes an innovative way of implementing such cross-border communities by linking distribution systems via a “switchable element”, a generation, storage, or consumption asset with a connection to each country. An optimization model has been developed to calculate the system cost savings of such a connection. Linking regions with complementary characteristics regarding electricity generation and demand via a switchable element leads to more efficient system utilization. Findings are relevant for the transposition of “citizen energy communities” in national laws

FlexEnergy - A Prosumer-based Approach For The Automated Marketing Of Manufacturing Companies' Energy Flexibility

The transition to renewable energy sources and the need to address climate change has significantly changed the energy landscape. However, the fluctuating nature of renewables and increased electricity price volatility pose challenges to power grids and companies. This study focuses on energy flexibility achieved through industrial demand-side management (DSM) as a solution. Information technology (IT) and standardization are vital for enabling energy flexibility by optimizing energy consumption and facilitating interoperability. Digital energy platforms allow energy-intensive industries to optimize energy usage, thus enabling industrial demand optimization and effective communication within the energy ecosystem. Standardization ensures the efficient implementation of energy flexibilitymeasures across diverse energymarkets.Thisstudy proposes a process model to streamline the integration of energy flexibility measures into production processes. This model eliminates the labor-intensive manual implementation process, enabling seamless adoption of energy flexibility measures and participation in energy markets. Marketing energy flexibility is addressed through the prosumer-based process that leverages standardized communication facilitated by the energy flexibility data model (EFDM), optimizing the energy consumption of manufacturing companies. The contributions of this research lie in the proposed processmodelfor marketing energy flexibility,streamlining energy flexibility implementation through automated EFDM modeling. The findings provide insights for researchers and practitioners, guiding the adoption of energy flexibility measures and supporting a sustainable energy future

Optimal strategy of electricity and natural gas aggregators in the energy and balance markets

This paper presents a stochastic two-stage model for energy aggregators (EAs) in the energy and balancing markets to supply electricity and natural gas to end-users equipped with combined heat and power (CHP) units. The suggested model takes into account the battery energy storage (BES) as a self-generating unit of EA. The upper and lower subproblems determine the optimal energy supply strategy of EA and consumption of consumers, respectively. In the lower subproblem, the McCormick relaxation is used to linearize the cost function of the CHP unit. To solve the proposed model, the two-stage problem is transformed into a linear single-stage problem using the KKT conditions of the lower subproblem, the Big M method, and the strong duality theory. The performance and efficiency of the proposed model are evaluated using a case study and three scenarios. According to the simulation results, adding CHP units to the energy-scheduling problem of BES-owned aggregators increases the profit of EA by 5.96% and decreases the cost of consumers by 1.57%.This work has received funding from the EU Horizon 2020 research and innovation program under project TradeRES (grant agreement No 864276). Pedro Faria is supported by FCT, grant CEECIND/01423/2021. The authors acknowledge the work facilities and equipment provided by GECAD research center (UIDB/00760/ 2020) to the project team.info:eu-repo/semantics/publishedVersio

A robust model for aggregated bidding of energy storages and wind resources in the joint energy and reserve markets

The high reliability and flexibility of Battery Energy Storage (BES) resources in comparison with other renewable technologies promote the development of this technology in smart grids. The fast response of BES to load variations could help the power system operators to maintain the balance of generation and consumption in real-time, and improve the flexibility of the smart grid, effectively. In this work, a new model is presented that determines the aggregated scheduling of BES and Wind Power Resource (WPR) in the joint energy and reserve markets. To evaluate the performance of BES in different markets, the proposed model is divided into day-ahead and real-time planning horizons. According to market prices, ramp rates, marginal costs, and technical constraints of units, the optimal participation levels in different markets are determined. The deployed power in real-time and wind power are considered as the uncertain parameters and the Robust Optimization (RO) framework is proposed to manage the related financial risk based on the worst-case realizations of uncertain parameters. The robust strategy is formulated based on the Mixed Integer Linear Programming (MILP) technique, which can be solved via the branch-and-bound method. Finally, the performance and effectiveness of the model are analyzed via different case studies. Simulation results show that the day-ahead and real-time markets are the best options for buying and selling the energy of BESs, and participation in the reserve market and regulation service increases their profit, significantly. Furthermore, the expected profit greatly depends on the risk preferences of decision-makers, and reducing the variation interval of wind generation by 40 % leads to an increase of 74.65 % in revenues.The present work has received funding from the European Regional Development Fund (FEDER) through the Northern Regional Operational Program, under the PORTUGAL 2020 Partnership Agreement and the terms of the NORTE-45-2020-75 call - Support System for Scientific and Technological Research - "Structured R&D&I Projects" - Horizon Europe, within project RETINA (NORTE 01-0145-FEDER-000062), we also acknowledge the work facilities and equipment provided by GECAD research center (UIDB/ 00760/2020) to the project team.info:eu-repo/semantics/publishedVersio

Reviewing and exploring the qualitative impacts that different market and regulatory measures can have on encouraging energy communities based on their organizational structure

The emergence of energy communities represents a promising option to democratize the energy system by empowering consumers to take a more active role. This can aid in achieving energy and environmental goals as well as encouraging more equitable distribution of costs and revenues between all parties on the energy system. Despite this potential, energy communities are still a nascent solution, the success of which is heavily influenced by regulations. As a result, there are a wide variety of organizational structures for energy communities at this time. This paper provides a review of the policy landscape in Spain as it relates to energy communities. This work also presents a formalized method for characterizing different energy community structures and provides a qualitative assessment of the impacts of different measures to encourage energy communities with respect to their organizational structure. Findings suggest that many market-focused measures, including wholesale, local flexibility, capacity, and multisector market measures favor larger, more integrated communities, while regulatory, legal, and organizational measures, including peer-to-peer trading, aggregation, and self-consumption favor smaller, more distributed communities. Additionally, when developing policies to encourage the growth of energy communities, policymakers should be cognizant of the progression of policies in the context of the desired outcomes for energy community growth specific to the region or country and its goals.Peer ReviewedPostprint (published version

Sizing and Management of Energy Storage Systems in Large-Scale Power Plants Using Price Control and Artificial Intelligence

Energy storage systems are expected to play a fundamental part in the integration of increasing renewable energy sources into the electric system. They are already used in power plants for different purposes, such as absorbing the effect of intermittent energy sources or providing ancillary services. For this reason, it is imperative to research managing and sizing methods that make power plants with storage viable and profitable projects. In this paper, a managing method is presented, where particle swarm optimisation is used to reach maximum profits. This method is compared to expert systems, proving that the former achieves better results, while respecting similar rules. The paper further presents a sizing method which uses the previous one to make the power plant as profitable as possible. Finally, both methods are tested through simulations to show their potential.Unión Europea Subvención 771066

Methodology for forecasting in the Swedish–Norwegian market for el-certificates

In this paper we describe a novel methodology for forecasting in the Swedish-Norwegian el-certificate market, which is a variant of a tradable green certificate scheme. For the forecasting, the el-certificate market is integrated in the electricity-market model EMPS, which has weekly to hourly time-step length, whereas the planning horizon can be several years. Strategies for the certificate inventory are calculated by stochastic dynamic programming, whereas penalty-rates for non-compliance during the annual settlement of certificates are determined endogenously.In the paper the methodology is described, and we show the performance of the model under different cases that can occur in the el-certificate market. The general results correspond to theoretical findings in previous studies for tradable green certificate markets, in particular that price-scenarios spread out in such a way that the unconditional expected value of certificates is relatively stable throughout the planning period. In addition the presented methodologies allows to assess the actual dynamics of the certificate price due to climatic uncertainty. Finally, special cases are indentified where the certificate price becomes excessively high respectively zero, due the design-specific dynamics of the penalty rate. © 2015 Elsevier Ltd.Methodology for forecasting in the Swedish–Norwegian market for el-certificatesacceptedVersio

Piloting Demand Response in Retailing : Lessons Learned in Real-Life Context

This article presents a case study on a demand response (DR) pilot project dealing with the application of DR in a grocery store with the utilization of refrigeration equipment as energy storage and photovoltaics (PV) as an energy source. DR has recently gained increased interest due to the growing penetration of intermittent renewable energy requiring flexibility in power consumption. The smart power grid enables the introduction of novel solutions to increase flexibility and the entrance of new actors into the markets. Developing new solutions for the mainstream markets requires experimentation in real-life settings serving the development of technological capabilities, necessary policies and regulation, and user and market needs, as well as adaptation of and to infrastructure and maintenance systems. Our case study on a DR pilot in a grocery store in Northern Finland focuses on how the project contributes to knowledge on the potential for DR and scaling up. It was found that energy efficiency, DR, and self-generated PV power can be aligned and even enhance the potential for DR. While mature technologies exist, applications and installations have not yet been standardized to enable rapid scaling up, and current DR market rules and practices fail to accommodate for small electricity consumers.Peer reviewe