Stochastic Coordination of Joint Wind and Photovoltaic Systems with Energy Storage in Day-Ahead Market

This paper presents an optimal bid submission in a day-ahead electricity market for the problem of joint operation of wind with photovoltaic power systems having an energy storage device. Uncertainty not only due to the electricity market price, but also due to wind and photovoltaic powers is one of the main characteristics of this submission. The problem is formulated as a two-stage stochastic programming problem. The optimal bids and the energy flow in the batteries are the first-stage variables and the energy deviation is the second stage variable of the problem. Energy storage is a way to harness renewable energy conversion, allowing the store and discharge of energy at conveniently market prices. A case study with data from the Iberian day-ahead electricity market is presented and a comparison between joint and disjoint operations is discussed

Stochastic optimization for the daily joint operation of wind/PV and energy storage

This paper deals with the problem of optimal bidding in a day-ahead market of electricity for a power producer having joint operation of wind with photovoltaic power systems and storage of energy. Uncertainty, not only on electricity market prices, but also on wind and photovoltaic powers, has to be faced in order to achieve optimal bidding. The problem is viewed as a sort of a two-stage stochastic optimization problem formulated by mix-integer linear programming. A case study with data from the Iberian Peninsula is presented and a comparison between joint and disjoint operations is discussed, allowing concluding that the joint operation attenuates the economic impact of disjoint operation volatility

Grid-connected renewables, storage and the UK electricity market

This article is a critical counterpoint to an article by published by Swift-Hook in the journal of Renewable Energy entitled "Grid-connected intermittent renewables are the last to be stored". In contrast to Swift-Hook we found evidence that "grid-connected intermittent renewables" have been, and will continue to be stored when it suits the "UK market" to do so.  This article is important to policy makers as energy storage (through EV battery demand side management for example) may well have an important role to play in facilitating the integration of high wind penetrations

The 2016 Power Trading Agent Competition

This is the specification for the Power Trading Agent Competition for 2016 (Power TAC 2016). 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; the winner of an individual “game” is the broker with the highest bank balance at the end of a simulation run. 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 approximate locational-marginal pricing through a simple manipulation of the wholesale supply curve. Customer models include households, electric vehicles, and a variety of commercial and industrial entities, many o

The 2015 Power Trading Agent Competition

This is the specification for the Power Trading Agent Competition for 2015 (Power TAC 2015). 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 dis

Temporal flexibility options in electricity market simulation models: Deliverable D4.1

Project TradeRES - New Markets Design & Models for 100% Renewable Power Systems: https://traderes.eu/about/ABSTRACT: This report covers the implementation of temporal flexibility options in TradeRES’ agent-based electricity market simulations models. Within this project, the term “temporal flexibility option” was defined as an asset or measure supporting the power system to balance electric demand and supply and compensate for their stochastic fluctuations stemming from, e.g., weather or consumer behaviour by adjusting demand and/or supply as a function over time or by reducing their forecast uncertainty. Other reports from the same work package of TradeRES are published almost simultaneously, each focussing on another aspect of market model enhancements. These accompanying reports address sectoral flexibility, spatial flexibility, actor types, and modelling requirements for market designs. Flexibility options covered in this report were selected with regard to a predominantly temporal characteristic, a contribution to TradeRES’ assessment of market designs, and the feasibility to be implemented in at least one of the agent based models (ABM) during the project’s lifetime. The technical aspects of “Load shedding”, “Load shifting”, “Electricity storage”, and “Real-time pricing” were selected for implementation. In addition, the following new electricity market products were selected for implementation: “Rolling market clearing”, “Trading with shorter time units”, and “Variable market closure lead times”.N/

An Exchange Mechanism to Coordinate Flexibility in Residential Energy Cooperatives

Energy cooperatives (ECs) such as residential and industrial microgrids have the potential to mitigate increasing fluctuations in renewable electricity generation, but only if their joint response is coordinated. However, the coordination and control of independently operated flexible resources (e.g., storage, demand response) imposes critical challenges arising from the heterogeneity of the resources, conflict of interests, and impact on the grid. Correspondingly, overcoming these challenges with a general and fair yet efficient exchange mechanism that coordinates these distributed resources will accommodate renewable fluctuations on a local level, thereby supporting the energy transition. In this paper, we introduce such an exchange mechanism. It incorporates a payment structure that encourages prosumers to participate in the exchange by increasing their utility above baseline alternatives. The allocation from the proposed mechanism increases the system efficiency (utilitarian social welfare) and distributes profits more fairly (measured by Nash social welfare) than individual flexibility activation. A case study analyzing the mechanism performance and resulting payments in numerical experiments over real demand and generation profiles of the Pecan Street dataset elucidates the efficacy to promote cooperation between co-located flexibilities in residential cooperatives through local exchange.Comment: Accepted in IEEE ICIT 201

Utilization of Electric Prosumer Flexibility Incentivized by Spot and Balancing Markets

The use of energy flexibility to balance electricity demand and supply is becoming increasingly important due to the growing share of fluctuating energy sources. Electric flexibility regarding time or magnitude of consumption can be offered in the form of different products on electricity spot and balancing power markets. In the wake of the energy transition and because of new possibilities provided by digitalization, the decision intervals on these markets are becoming shorter and the controllability of electricity consumption and generation more small-scale. This evolution opens up new chances for formerly passive energy consumers. This thesis shows how electric flexibility can be monetized using the application example of commercial sites. These are often multimodal energy systems coupling electricity, heat, and gas, and thus deliver high flexibility potential. To leverage this potential, a comprehensive picture of demand-side flexibilization is provided and used to propose an energy management system and optimization for cost-optimized device schedules. The cost-optimization considers two simultaneous incentives: variable day-ahead spot market prices and revenues for offering possible schedule adjustments to the automatic Frequency Restoration Reserve (aFRR) balancing market. To solve the formulated optimization problem, a genetic algorithm is presented, tailored to the specific needs of consumers. In addition to addressing the trade-off between the two competing markets, the algorithm inherently considers the uncertain activation of aFRR bids and related catch-up effects. An analysis of the activation behavior of aFRR balancing market bids, based on a developed ex-post simulation, forms an important decision basis for the optimization. Finally, a simulation study concentrating on battery energy storage systems and combined heat and power plants on the consumer side enables the quantitative discussion of the optimization potential. The results show that consumers considering both markets simultaneously can achieve cost benefits that are up to multiples of those for pure day-ahead price optimization, despite the stochastic nature of aFRR balancing power activations. In conclusion, this thesis enables formerly passive electricity consumers to assume the role of alternative balancing service providers, hence contributing to the economic and reliable operation of power grids characterized by a high share of renewable energy sources

The 2017 Power Trading Agent Competition

This is the specification for the Power Trading Agent Competition for 2017 (Power TAC 2017). 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; the winner of an individual “game” is the broker with the highest bank balance at the end of a simulation run. 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 approximate locational-marginal pricing through a simple manipulation of the wholesale supply curve. Customer models include households, electric vehicles, and a variety of commercial and industrial entities, many of which have production capacity such as solar panels or wind turbines. All have “real-time” metering to support allocation of their hourly supply and demand to their subscribed brokers, and all are approximate ut