Participation In Electricity Research Articles

Conformal prediction for stochastic decision-making of PV power in electricity markets

This paper studies the use of conformal prediction (CP), an emerging probabilistic forecasting method, for day-ahead photovoltaic power predictions to enhance participation in electricity markets. First, machine learning models are used to construct point predictions. Thereafter, several variants of CP are implemented to quantify the uncertainty of those predictions by creating CP intervals and cumulative distribution functions. Optimal quantity bids for the electricity market are estimated using several bidding strategies under uncertainty, namely: trust-the-forecast, worst-case, Newsvendor and expected utility maximization (EUM). Results show that CP in combination with k-nearest neighbors and/or Mondrian binning outperforms its corresponding linear quantile regressors. Using CP in combination with certain bidding strategies can yield high profit with minimal energy imbalance. In concrete, using conformal predictive systems with k-nearest neighbors and Mondrian binning after random forest regression yields the best profit and imbalance regardless of the decision-making strategy. Combining this uncertainty quantification method with the EUM strategy with conditional value at risk (CVaR) can yield up to 93% of the potential profit with minimal energy imbalance.

Forecasting electricity demand of municipalities through artificial neural networks and metered supply point classification

This study develops a methodology to characterise and forecast large consumers’ electricity demand, particularly municipalities, with hundreds of different metered supply points based on the previous characterisation of facilities’ consumption. Demand forecasting allows consumers to improve their participation in electricity markets and manage their electricity consumption. The method considers a classification by different types of metered supply points combined with artificial neural networks to obtain hourly forecasts using well-known parameters such as day types, hourly temperature, the last hour of electricity consumption, and sunrise and sunset time. We apply the methodology to the municipality of Valencia using over five hundred hourly load profiles for a year during 2017 and 2018. Our results present aggregated forecasts with a maximum Mean Absolute Percentage Error of 3.8% per day, outperforming the same forecast without classifying Metered Supply Points. We conclude that a correct electricity demand forecast for a consumer with different types of consumption does not need submetering, but characterising Metered Supply Points is an option with lower costs that allows for better predictions.

Receding horizon dispatch of multi-period look-ahead market for energy storage integration

Although noteworthy efforts towards energy storage participation in electricity markets have been witnessed worldwide during the last decades, market mechanisms for optimal storage integration are yet to be comprehensively studied. This paper presents a novel approach wherein a multi-period look-ahead market is proposed to progressively solve the optimisation problem over a receding horizon, incorporating intertemporal constraints to enable forward planning. The focus is specifically on modelling the bidding behaviour of energy storage, and to address this, we develop two distinct models for the participation of energy storage in the market. The integrated model aims to schedule the future dispatch of the storage to minimise total operational cost, taking into account the fixed costs of the storage, while the strategic model pursues the dual ambition to maximise storage profit by formulating a bilevel optimisation problem. We validate the viability of our proposed market mechanisms through case studies on the Australian National Electricity Market. We conduct case studies which provide evidence that both the strategic and integrated bidding models effectively address the requirement for forward planning, resulting in reduced market costs and volatility. Notably, the strategic model outperforms in terms of generating higher storage revenue, while the integrated model exhibits a slight advantage in terms of lower market costs compared to the strategic model.

The role of electricity market design for energy storage in cost-efficient decarbonization

The role of electricity market design for energy storage in cost-efficient decarbonization

Optimal energy management of integrated energy systems for strategic participation in competitive electricity markets

The uncertainties originating from electricity markets, renewable distributed generations (RDG) and multi-energy loads pose huge challenges to the optimal energy management of the integrated energy system (IES) in competitive electricity markets. This paper develops an energy management framework (EMF) with a two-stage process for IES strategic participation in electricity markets, which can coordinate the operation of IES to comply with the two-settlement mechanism of the day-ahead and real-time markets. The former stage incorporates the conditional value-at-risk to make day-ahead bidding decisions for risk aversion towards uncertainties introduced by the electricity market. The latter coordinates the day-ahead bidding electricity to implement energy dispatch of IES for real-time market participation. Furthermore, supply-demand balances are formulated into chance constraints in the day-ahead bidding while real-time energy dispatch is implemented based on rolling model predictive control in response to uncertainties due to RDG and multi-energy loads. The proposed EMF is extensively assessed through two case studies in the PJM and Guangdong electricity markets. The numerical results demonstrate that the proposed EMF can effectively achieve day-ahead bidding of IES with risk aversion towards unexpected increases in operating costs caused by market uncertainties and determine the optimal operation scheme for its participation in the real-time market.

Equilibrium analysis for prosumer’s participation in joint electricity and carbon markets

The current electricity system has a large number of carbon emissions. As an environmentally friendly and low-carbon electricity generation source, distributed energy sources gradually get more and more attention. However, the participation of a large number of distributed energy sources brings uncertainty to the operation of the electricity market. Prosumers could effectively avoid the instability caused by the participation of distributed energy sources in the electricity market. At present, the strategy of the prosumer participating in the electricity market has been thoroughly studied. However, equilibrium analysis for prosumer’s participation in joint electricity and carbon markets still needs to be studied in depth. Therefore, we propose a method for the prosumer to participate in the joint electricity and carbon markets, which could effectively reduce carbon emissions. First, we build a carbon market model. Then, the equilibrium model of the prosumer’s participation in joint electricity and carbon markets is constructed. The equilibrium model can realize the equilibrium between the coordination of prosumers and joint markets. Finally, we adopt an iterative algorithm to solve the equilibrium model. Prosumers and joint electricity and carbon markets transmit marginal information to each other. According to the marginal information. The equilibrium between Prosumer and joint markets is solved through iteration by self-optimization and self-decision. Case studies based on the IEEE 30-bus system verify the effectiveness of the model and method proposed in this paper.

An agent-based modelling framework for the simulation of large-scale consumer participation in electricity market ecosystems

The role of consumers as price-sensitive participants in electricity markets is considered essential to ensure efficient and secure operations of electricity systems. Yet the uncertain or unknown consequences of active market participation remain a large barrier for active consumer-side market participation. Simulations are a powerful tool to reduce this uncertainty by giving consumers an insight on the potential benefits and costs of market participation. However, the simulation setup must be adapted to each market context and each consumer market participation strategy. To simplify the simulation development process and improve the comparability of simulation results, this paper proposes a modular yet systematic electricity market modelling framework. The framework applies object-oriented programming concepts for business ecosystem modelling presented in previous works to develop an agent-based model of a consumer-centric electricity market ecosystem. The market ecosystem is represented by a multitude of interacting submarkets with their own logic. Within submarkets, context-independent and context-dependent elements are distinguished to provide model abstraction which can be adapted to different contexts. This framework is illustrated by applying it to three different submarkets in the Western Danish electricity market context: the Nordpool day-ahead market, the Nordpool intraday market, and the Frequency Containment Reserve market. The submarket role abstractions allow to benefit from the commonalities between the analysed submarkets during model implementation, while the role parametrisations allow to quickly adapt the roles to each market context. The implementation of the modelling framework in the Nordic context highlights the benefits of a modular approach in a liberalised and unbundled market context.

Community participation in electricity markets: The impact of market organisation

Energy communities are considered important drivers of the energy transition. While they have mainly focused on collective energy generation, they are increasingly getting involved in the provision and trading of energy and in flexibility services. This change of focus creates opportunities to participate in electricity markets but proves to be challenging because of the current electricity markets serving incumbency. In this study, we conduct an in-depth analysis of how broader market organisation impacts participation of energy communities in electricity markets. Drawing from transition studies and economic market literature, we develop a framework for analysing market organisation. Based on a literature review, observations and semi-structured interviews with communities and market actors, we conclude that, as a result of conflicting community and market logic, communities often adjust their business models to participate in the markets, which could jeopardize their potential to contribute to radical system change.

Operation strategy and profitability analysis of independent energy storage participating in electricity market: A provincial case study in China

The new energy storage, referring to new types of electrical energy storage other than pumped storage, has excellent value in the power system and can provide corresponding bids in various types of electricity markets. As the scale of new energy storage continues to grow, China has issued several policies to encourage its application and participation in electricity markets. It is urgent to establish market mechanisms well adapted to energy storage participation and study the operation strategy and profitability of energy storage. Based on the development of the electricity market in a provincial region of China, this paper designs mechanisms for independent energy storage to participate in various markets. Then, its current and future operation strategies by division time or capacity for participation in each type of market are analyzed, and the profitability under various scenarios is calculated. Finally, based on the calculation results, the theoretical analysis basis for developing independent energy storage in the province and the policy formulation of participation in the market is provided.

A Review of Modeling, Management, and Applications of Grid-Connected Li-Ion Battery Storage Systems

The intermittency of renewable energy sources makes the use of energy storage systems (ESSs) indispensable in modern power grids for supply-demand balancing and reliability enhancement. Besides pumped-storage hydroelectric power stations, energy storage deployment worldwide is still quite low. However, the status quo might rapidly change as the energy storage technologies are growing and facilitating market regulations are being ratified. Battery energy storage systems (BESSs), Li-ion batteries in particular, possess attractive properties and are taking over other types of storage technologies. Thus, in this article, we review and evaluate the current state of the art in managing grid-connected Li-ion BESSs and their participation in electricity markets. The review mainly includes battery modeling, the architecture of battery management systems (BMSs), the incorporation of BESSs for electricity market services, global utility-scale battery storage facilities, and challenges in implementing and managing grid-connected BESSs.

Virtual Power Plant Operational Strategies: Models, Markets, Optimization, Challenges, and Opportunities

High penetration of distributed generation and renewable energy sources in power systems has created control challenges in the network, which requires the coordinated management of these resources. Using virtual power plants (VPPs) on a large scale has solved these challenges to a significant extent. VPPs can be considered systems consisting of distributed generations, energy storage, controllable loads, electric vehicles (EVs), and other types of resources to provide energy and ancillary services. VPPs face various challenges such as energy management, operation, resource uncertainty, participation in electricity markets, etc. This paper discusses an overview of the basic challenges of VPPs, including control and communication issues, electricity markets, its different models, and energy management issues. The main purpose is to investigate the performance of VPP in different markets, energy management of VPP in different operating conditions and strategies, and compare different planning methods for VPP. Note that the application of blockchain to control and improve VPP performance has been investigated, taking into account the different layers of this technology.

Prosumers Flexibility as Support for Ancillary Services in Low Voltage Level

The prosumers flexibility procurement has increased due to the current penetration of distributed and variable renewable energy sources. The prosumers flexibility is often able to quickly adjust the power consumption, making it well suited as a primary and secondary reserve for ancillary services. In the era of smart grids, the role of the aggregator has been increasingly exploited and considered as a player that can facilitate small prosumers' participation in electricity markets. This paper proposes an approach based on the use of prosumers flexibility by an aggregator to support ancillary services at a low voltage level. An asymmetric pool market approach is considered for flexibility negotiation between prosumers and the local marker operator (aggregator). From the achieved results it is possible to conclude that the use of flexibility can bring technical and economic benefits for network operators.

An Asynchronous Online Negotiation Mechanism for Real-Time Peer-to-Peer Electricity Markets

Participants in electricity markets are becoming more proactive owing to the fast deployment of distributed energy resources (DERs) and the further development of demand-side management (DSM), which also boosts the emergence of Peer-to-Peer (P2P) market mechanisms. Moreover, the market is also required to operate in a real-time scheme in response to changes in generation and load to maintain power balance. It is highly desirable to propose and analyse novel approaches suitable for real-time P2P market mechanisms. These are challenging since most often involving a heavy computational burden, while the time available for negotiation in real-time is very short. Our core contribution is to design and analyse a novel asynchronous online optimization framework and related real-time P2P market negotiation mechanism, which can greatly reduce the computation and communication burden from two aspects. First, a novel online consensus alternating direction method of multipliers (ADMM) algorithm is proposed. It significantly reduces computation since only one iteration is performed for each agent at every time period. Second, the market operates in an asynchronous mode so that all agents can freely trade without waiting for idle or inactive neighboring agents. The sublinear regret upper bound is proved for our asynchronous online algorithm, which indicates that social welfare in the market can be maximized in the long run on average (over time). Simulations show that our algorithm enjoys good convergence performance, robustness, and fairness.

Bidding strategy for a prosumer aggregator with stochastic renewable energy production in energy and reserve markets

A growing body of electricity consumers with renewable energy and demand response resources have become active prosumers who can both produce and consume energy. However, the small size of most prosumers forbids their direct participation in electricity markets. This study addresses this issue by proposing a bidding strategy for the prosumer aggregator based on a bi-level model to enable multiple prosumers to participate in day-ahead energy and reserve markets as a single entity. Here, the prosumer aggregator profit and social welfare are maximized in the upper-level and lower-level models, respectively, where the heating, ventilation, and air-conditioning system loads, residential loads, energy outputs associated with photovoltaic power generators, and energy storage systems are included. The Karush–Kuhn–Tucker condition and strong duality equalities are used to transform the proposed bi-level model into a single-level model. The effectiveness and economy of the proposed approach are demonstrated based on the numerical results obtained for a realistic test system.

Dynamic Virtual Power Plant: A New Concept for Grid Integration of Renewable Energy Sources

The concept of Virtual Power Plant (VPP) has arisen over a decade ago from the relatively low competitiveness of the back then emerging non-dispatchable RES. A set of smaller generators imitates the behavior of large synchronous generators. So far, static aspects such as generation or slow dynamics have been of interest, as it is the case for the zonal secondary frequency control scheme in Spain, which can be viewed as a VPP. However, considering dynamic aspects is of high importance, especially to further increase the current penetration level of Renewable Energy Sources (RES). Indeed, one should deal with the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">full</i> participation of RES in grid ancillary services. This means not only to get some positive impact on grid voltage and frequency dynamics but to bring concepts which allow integrating RES to existing secondary regulation schemes on the same level as classic synchronous generators. For that, we propose here a new concept called Dynamic VPP (DVPP) which fully integrates the dynamic aspects at all levels: locally (for each RES generator), globally (for grid ancillary services and interaction with other close-by elements of the grid) and economically (for internal optimal dispatch and participation in electricity markets). A DVPP is a set of dispatchable and non-dispatchable RES along with a set of common control and operation procedures. The latter procedures include the choice of dispatchable and non-dispatchable RES constituting the DVPP, the control of DVPP generators for local objectives and participation of the DVPP as a single unit in ancillary services (especially in case of loss of natural resources - e.g., wind, sun - on a part of the DVPP), the limitation of the risk of adversely interaction with close-by elements and the feasibility in both current power systems scenarios and future ones with large share of RES. This new DVPP framework and approaches developed for its implementation allows ensuring optimal operation of a mixed portfolio of dispatchable and non-dispatchable RES generators for planning, participation to the markets and real-time control. For the control, all time-scale dynamics are considered to improve RES management (internal re-dispatch inside the DVPP to take advantage of dispatchable/non-dispatchable nature of each RES and to optimally manage the lack of natural resources in some regions of the DVPP) and their participation to grid ancillary services. Concrete structures of DVPP as well as ways to address the other control and economical aspects will be shown. This new DVPP concept is now under development in the H2020 POSYTYF project (https://posytyf-h2020.eu/).

Community Participation in Electricity Markets: The Impact of Market Organisation

Community Participation in Electricity Markets: The Impact of Market Organisation

Optimisation for Coalitions Formation Considering the Fairness in Flexibility Market Participation

This paper proposes a coalitional game-theoretical model for consumers’ flexibility coalition formation, supported by an optimization model based on differential evolution. Traditionally, the participation in conventional electricity markets used to be limited to large producers and consumers. The final end-users contract their energy supply with retailers, since due to the smaller quantity available for trading, they cannot participate in electricity market transactions. Nowadays, the growing concept of local electricity market brings many advantages to the end-users. The flexibility negotiation considering local areas is an important procedure for network operators and it is incorporating a local electricity market opportunity. A coalition formation model to facilitate small players participation in the flexibility market proposed by the network operator is addressed in this work. The inclusion of Shapley value in the proposed model enables finding the best coalition structures considering the fairness of the coalitions in addition to the potential income achieved by the consumers when selling their flexibility. An optimization model based on differential evolution is also proposed as the way to find the optimal coalition structures based on the multi-criteria specifications.

Integrated data‐driven framework for fast SCUC calculation

Security-constrained unit commitment (SCUC) is one of the most important optimisation problems for operation planning of power systems. Indeed, with the fast expansion of power grids and the increasing growth of heterogeneous market participants in electricity markets, the day-ahead SCUC continuously encounters significant performance challenges. To improve the computational performance of SCUC and achieve solutions of good quality, an integrated framework which combines a data-driven approach and a variable-aggregation method is presented in this study. The variable-aggregation method effectively approximates the original network security constraints with a reduced number of variables. Moreover, as aggregated constraints could reduce the feasible region of the original SCUC and potentially degrade solution quality for certain SCUC instances, it is preferable to only apply such an aggregation approach towards difficult SCUC instances. Therefore, a data-driven classification method, by adopting relevant SCUC input data as features, is integrated to first predict whether a SCUC instance is ‘easy’ or ‘hard’. To this end, the integrated framework could improve the overall performance of SCUC instances, by and large, in terms of computational efficiency and solution quality. Numerical results illustrate the effectiveness of the proposed integrated framework.

Optimal design of an electricity-intensive industrial facility subject to electricity price uncertainty: Stochastic optimization and scenario reduction

When considering the design of electricity-intensive industrial processes, a challenge is that future electricity prices are highly uncertain. Design decisions made before construction can affect operations decades into the future. We thus explore whether including electricity price uncertainty into the design process affects design decisions.We apply stochastic optimization to the design and operations of a chlor-alkali plant, an electrochemical process that produces chlorine, caustic soda, and hydrogen. Chlor-alkali production is electricity intensive and can be operated flexibly based on fluctuating electricity prices. We consider participation in the 5-min real time market and consider each day as a scenario in the stochastic program.We find that flexible plant designs that oversize certain plant components can enhance participation in electricity markets and increase profits. When electricity-price uncertainty is considered by using stochastic optimization, the optimal system design includes fuel cell and hydrogen storage capacity, which allow the plant to hedge against price uncertainty. We furthermore find that scenario reduction techniques, which are used to reduce computational complexity, in our example approximate the expected objective function value well, but lead to error in terms of optimal design decision variables. This error ranges from not building some components (fuel cell and hydrogen storage capacity) to overestimating their capacities by 50%.

Wind Power Long-Term Scenario Generation Considering Spatial-Temporal Dependencies in Coupled Electricity Markets

Wind power has been increasing its participation in electricity markets in many countries around the world. Due to its economical and environmental benefits, wind power generation is one of the most powerful technologies to deal with global warming and climate change. However, as wind power grows, uncertainty in power supply increases due to wind intermittence. In this context, accurate wind power scenarios are needed to guide decision-making in power systems. In this paper, a novel methodology to generate realistic wind power scenarios for the long term is proposed. Unlike most of the literature that tackles this problem, this paper is focused on the generation of realistic wind power production scenarios in the long term. Moreover, spatial-temporal dependencies in multi-area markets have been considered. The results show that capturing the dependencies at the monthly level could improve the quality of scenarios at different time scales. In addition, an evaluation at different time scales is needed to select the best approach in terms of the distribution functions of the generated scenarios. To evaluate the proposed methodology, several tests have been made using real data of wind power generation for Spain, Portugal and France.