Management of local citizen energy communities and bilateral contracting in multi-agent electricity markets

ABSTRACT: Over the last few decades, the electricity sector has experienced several changes, resulting in different electricity markets (EMs) models and paradigms. In particular, liberalization has led to the establishment of a wholesale market for electricity generation and a retail market for electricity retailing. In competitive EMs, customers can do the following: freely choose their electricity suppliers; invest in variable renewable energy such as solar photovoltaic; become prosumers; or form local alliances such as Citizen Energy Communities (CECs). Trading of electricity can be done in spot and derivatives markets, or by bilateral contracts. This article focuses on CECs. Specifically, it presents how agent-based local consumers can form alliances as CECs, manage their resources, and trade on EMs. It also presents a review of how agent-based systems can model and support the formation and interaction of alliances in the electricity sector. The CEC can trade electricity directly with sellers through private bilateral agreements. During the negotiation of private bilateral contracts, the CEC receives the prices and volumes of their members and according to its negotiation strategy, tries to satisfy the electricity demands of all members and reduce their costs for electricity.info:eu-repo/semantics/publishedVersio

Agent-based model of citizen energy communities used to negotiate bilateral contracts in electricity markets

ABSTRACT: The worldwide targets for carbon-neutral societies increased the penetration of distributed generation and storage. Smart cities now play a key role in achieving these targets by considering the alliances of their demand and supply assets as local citizen energy communities. These communities need to have enough weight to trade electricity in wholesale markets. Trading of electricity can be done in spot markets or by bilateral contracts involving customers and suppliers. This paper is devoted to bilateral contracting, which is modeled as a negotiation process involving an iterative exchange of offers and counter-offers. This article focuses on local citizen energy communities. Specifically, it presents team and single-agent negotiation models, where each member has its sets of strategies and tactics and also its decision model. Community agents are equipped with intra-team strategies and decision protocols. To evaluate the benefits of CECs, models of both coalition formation and management have been adapted. This paper also describes a case study on forward bilateral contracts, involving a retailer agent and three different types of citizen energy communities. The results demonstrate the benefits of CECs during the negotiation of private bilateral contracts of electricity. Furthermore, they also demonstrate that in the case of using a representative strategy, the selection of the mediator may be critical for achieving a good deal.info:eu-repo/semantics/publishedVersio

The Economic Sustainability of Variable Renewable Energy Considering the Negotiation of Different Support Schemes

ABSTRACT: The increase in the prices of fossil fuels and environmental issues are leading to a high investment in wind power and solar photovoltaic all over Europe, reducing its dependence on imported fossil fuels. The European countries started incentive programs for investment in these renewable technologies, which consisted of fixed and market premium feed-in tariffs. These feed-in schemes involve long-term contracts with updated prices over inflation. These incentives highly increase the investment and installation of new renewable capacity in Europe. They lead to high renewable penetrations in power systems but originate a tariff deficit due to the difference between market prices and the tariffs paid to these technologies. End-use consumers pay the tariff deficit on retail tariffs. This work analyzes the market-based remuneration of variable renewable energy considering different support schemes and the role of risk-sharing contracts in mitigating the spot price volatility. It presents models able to negotiate bilateral contracts considering risk management, notably risk attitude and risk sharing, bid establishment, and clause (by-laws) negotiation. Furthermore, to evaluate the economic sustainability of renewable generation in Spain, it presents a study for different 12-year support schemes starting in 2010. The results confirmed that, in the case of using risk-sharing contracts during crisis periods, the incidence of low energy prices (price "cannibalization") decreases, such as the tariff deficit. Furthermore, in the case of high-inflation periods, these contracts hedge against the increase in retail prices, resulting in an economic surplus for consumers.info:eu-repo/semantics/publishedVersio

The 2012 Power Trading Agent Competition

This is the specification for the Power Trading Agent Competition for 2012 (Power TAC 2012). Power TAC is a competitive simulation that models a “liberalized” retail electrical energy market, where competing business entities or “brokers” offer energy services to customers through tariff contracts, and must then serve those customers by trading in a wholesale market. Brokers are challenged to maximize their profits by buying and selling energy in the wholesale and retail markets, subject to fixed costs and constraints. Costs include fees for publication and withdrawal of tariffs, and distribution fees for transporting energy to their contracted customers. Costs are also incurred whenever there is an imbalance between a broker’s total contracted energy supply and demand within a given time slot. The simulation environment models a wholesale market, a regulated distribution utility, and a population of energy customers, situated in a real location on Earth during a specific period for which weather data is available. The wholesale market is a relatively simple call market, similar to many existing wholesale electric power markets, such as Nord Pool in Scandinavia or FERC markets in North America, but unlike the FERC markets we are modeling a single region, and therefore we do not model location-marginal pricing. Customer models include households and a variety of commercial and industrial entities, many of which have production capacity (such as solar panels or wind turbines) as well as electric vehicles. All have “real-time” metering to support allocation of their hourly supply and demand to their subscribed brokers, and all are approximate utility maximizers with respect to tariff selection, although the factors making up their utility functions may include aversion to change and complexity that can retard uptake of marginally better tariff offers. The distribution utility models the regulated natural monopoly that owns the regional distribution network, and is responsible for maintenance of its infrastructure and for real-time balancing of supply and demand. The balancing process is a market-based mechanism that uses economic incentives to encourage brokers to achieve balance within their portfolios of tariff subscribers and wholesale market positions, in the face of stochastic customer behaviors and weather-dependent renewable energy sources. The broker with the highest bank balance at the end of the simulation wins

An adaptive agent-based system for deregulated smart grids

The power grid is undergoing a major change due mainly to the increased penetration of renewables and novel digital instruments in the hands of the end users that help to monitor and shift their loads. Such transformation is only possible with the coupling of an information and communication technology infrastructure to the existing power distribution grid. Given the scale and the interoperability requirements of such future system, service-oriented architectures (SOAs) are seen as one of the reference models and are considered already in many of the proposed standards for the smart grid (e.g., IEC-62325 and OASIS eMIX). Beyond the technical issues of what the service-oriented architectures of the smart grid will look like, there is a pressing question about what the added value for the end user could be. Clearly, the operators need to guarantee availability and security of supply, but why should the end users care? In this paper, we explore a scenario in which the end users can both consume and produce small quantities of energy and can trade these quantities in an open and deregulated market. For the trading, they delegate software agents that can fully interoperate and interact with one another thus taking advantage of the SOA. In particular, the agents have strategies, inspired from game theory, to take advantage of a service-oriented smart grid market and give profit to their delegators, while implicitly helping balancing the power grid. The proposal is implemented with simulated agents and interaction with existing Web services. To show the advantage of the agent with strategies, we compare our approach with the “base” agent one by means of simulations, highlighting the advantages of the proposal

Trade in Energy Services - GATS and India

Energy plays a vital role in the development of any economy and given its unequal distribution trade in energy, especially fossil fuels, is an important component of international trade. In the past, due to its public good characteristics, energy-related services were mostly supplied by the government. With liberalization and globalization the sector underwent significant transformation. Many new services developed and large multinationals emerged which increased global trade in energy services. Energy services is now an important component of all trade agreements. In the above context, this paper examines Indias opportunities and constraints to trade in energy services within the GATS framework. The study found that India has the capability of exporting high-skilled manpower at competitive prices but is facing various market access, discriminatory and regulatory barriers in markets of export interest. With the entry of energy producing countries such as Saudi Arabia into the WTO, the Doha negotiations provide an important platform to offensively push for liberalization in this sector. India needs foreign investment, technical know-how and international best practices in energy. The country has progressively liberalized this sector and there are no major entry barriers. However, India has not been successful in attracting large foreign investment and technology. This is due to various domestic barriers which make it difficult to set up a competitive operation. The study lists the reform measures which will help the sector become globally competitive, protect the interests of consumers and meet the energy needs of society. Since this sector is sensitive and is closely monitored by governments across the world, government-to-government collaborations would ease the entry process for Indian companies in foreign markets, diversify our energy resource base and improve energy security.GATS, Energy, trade, India & the WTO

Agile Market Engineering: Bridging the gap between business concepts and running markets

The agile market engineering process model (AMEP) is built on the insight, that market design and development is a wicked problem. Electronic markets are too complex to be completely designed upfront. Instead, AMEP tries to bridge the gap between theoretic market design and practical electronic market platform development using an agile, iterative approach that relies on early customer feedback and continuous improvement. The AMEP model is complemented by several supporting software artifacts

Optimizing energy market participation with batteries

Due to the fact that the energy sector is in transition, there are goals for lowering the energy cost with the use of renewables and batteries. This presents challenges to the system and the solution is the issuing of energy communities that can be used to make electricity provision more clean and secure. It is also to see how energy flexibility elements or elements on the consumption side can make the system more efficient and cheaper, which is being done in this paper concerning the day-ahead bid and batteries. Traditional day-ahead bidding methods have become costly, mainly when the forecasted energy consumption differs from the actual consumption, which has to be resolved by penalizing with an imbalance cost. This thesis is part of a more significant project (Layered Energy System) that is to be deployed in Spain. Applying such changes to the electricity system first requires becoming familiar with and understanding Spain's context. The first part of this thesis provides research to understand the Spanish regulatory framework, how the market works, and the status of these technologies in Spain. Following that, this thesis's primary work is to explore how day-ahead market bid could be improved through the use of batteries for better planning and error assumptions. It mentions several day-ahead bidding strategies in the context of energy and batteries. And then selects a subset (three) of the studied strategies and implements them, comparing their performance on actual electricity data. Finally, selects the one that best fits various scenarios and requirements. A particular objective function is opted to be minimized with respect to the battery constraints that involve the variables. A linear program will find the values that best fits those variables at every time step $t$ of a single day. The methodology is an improvement over traditional predictive models. After comparing different strategies, Results show that strategy one, namely "Stochastic Chance-constraint optimization", yields the best results. In this strategy, the battery would have the freedom to maximize profit even if it sometimes increases imbalance. The preferred error distribution for this strategy is the Gamma distribution. Using a battery to offset imbalances can help to minimize total energy cost for a whole day (up to 26%). The last part of the thesis is ongoing research about capacity traders and market performance. It surveys the literature on trading strategies in various contexts and markets relevant to capacity traders. The market performance in capacity trading needs to consider how well the buildings can reach their desired capacity through bidding and selling. Performance metrics that are typically used to evaluate those trading strategies were documented. This feature is being worked on with python, but it will not be able to be shown

Multi agent system framework for demand response management in distribution networks

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe inexorable increase in penetration of clean energies and responsive loads in the Distribution Network (DN), introduces new technical challenges for network operators. The responsibilities of Distribution Network Operators (DNOs) are being adjusted to cope with current system challenges and they are transitioning to Distribution System Operators (DSOs), taking a more active role in dynamically managing power flows across the network. Further, the advancement in distribution automation technologies provides greater opportunities for energy consumers to take more effective participation in demand reduction schemes and DSOs can be enablers of Demand Response (DR). Hence, the functionality of DR can be considered an alternative, lower cost, carbon-saving and flexible solution to defer network reinforcement. This forms the rationale behind this thesis, which aims to provide an in-depth investigation of the potential responsiveness in residential demand and its effect on constraint management of the DN. The main contribution of this thesis is the design, development and implementation of a Multi Agent System (MAS) framework for active DN management through residential DR. One advantage of the proposed platform is the capability of integrating both centralised and decentralised DR control mechanisms. It employs the responsiveness of demand from both loads shifting and shedding through price-based and incentive-based DR respectively. The feasibility and effectiveness of such a platform has been evaluated by developing and implementing the DR mechanism in three levels. The DR algorithm for several static and dynamic electricity tariffs, (Time of Use (ToU), Day-Ahead (DA) and Real Time Pricing (RTP)), is designed and developed in Low Voltage (LV) feeders. This is then expanded and implemented in a Medium Voltage (MV) feeder under an RTP environment. Finally, two incentive-based DR schemes: emergency and local community DR, are merged in the MV/LV network to improve its reliability and security. The implementation of the MAS framework demonstrated its configurability and scalability through three case studies under different scenarios. One novel aspect of this research is the consideration of customers’ characteristics in the design of the DR algorithms. In addition, at MV level, the tariffs and the required DR are allocated to each LV feeder specifically taking into account their DR potential and participation effects on the overall network performance. The simulation results at LV level show that maximum peak demand reduction and the most flattened load profile are achieved with RTP. At MV/LV network, MAS provides a community environment where the consumers can collaborate to decrease their overall demand. Moreover, the local community can reduce their dependency on the grid during daytime with PV generation. It is concluded that DR trading can benefit all players economically and also lessen DN violations from stipulated limits