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Optimal bidding strategy of a plug-in electric vehicle aggregator in day-ahead electricity markets under uncertainty
... Ref. [4] tackled the problem of an aggregator bidding in the dayahead electricity market, aiming to minimize charging costs and meet the flexible demand of EVs. This work proposed an aggregated representation of EV end-use requirements as a virtual battery, characterized by time-varying power and energy constraints, based on individual driving patterns. ...
... As mentioned before, this work integrates features from prior models and incorporates novel modeling variations in comparison to [4]- [12]. Differing from [8]- [10], and [12], the proposed framework is based on scenario-based stochastic programming. ...
... Differing from [8]- [10], and [12], the proposed framework is based on scenario-based stochastic programming. Within this framework, a methodology to characterize the uncertainty in the operation of EV-based virtual batteries was developed, inspired by [4], [5] and [8], where the probability functions of the EV behavior are obtained from [11]. In addition to addressing the aforementioned uncertainty, this work also considers the uncertainty associated with renewable production, baseload demand, and prices in the power distribution system operation, unlike [6], [9]- [11], and [12]. ...
... An exact and finite decomposition algorithm is proposed to solve the problem in an iterative manner. [20] proposes a bilevel program for EVs aggregators from a different perspective. Instead of maximising profit at the upper level, charging cost minimisation is formulated. ...
... [25] develops a new scenario-based stochastic optimisation model for price-maker economic bidding in both day-ahead and real-time markets where a DR program with time-shiftable load is adopted to create load flexibility. [20] proposes an optimal bidding strategy for a large-scale plug-in electric vehicle (PEV) aggregator. The upper level represents the charging cost minimisation of the PEV aggregator, whereas the market-clearing problem is formulated at the lower level. ...
... Existing studies can be further categorised based on whether market players participate in multiple levels of markets (e.g., wholesale vs. local/ retail) simultaneously. Most studies, however, are often based on a single electricity market, such as dayahead market [4], [6], [7], [9], [16], [20], [21], [35] or retail market [5], [10], [34], [39], [42]. There are also a few studies that focus on analysing interactions among market participants in the wholesale (i.e., day-ahead and real-time) electricity markets [17], [25], [26]. ...
With the constant development of energy systems, multi-energy networks are becoming increasingly popular, which integrates renewable energies and demand-side management. This presents a significant challenge for developing smart energy management frameworks. Furthermore, unlike traditional energy systems, the transaction energy system is dynamic and complex, which is enriched by the interdependence among multiple energies, the uncertainty of renewable energies and the complexity of demand-side management. Therefore, advanced modelling techniques and solution methods are required to be developed to overcome the difficulties. This thesis addresses the intricate challenges of developing a smart hierarchical transactive energy system that seamlessly marries multi-energy sources, renewable energy integration, and sophisticated demand-side management strategies. The research unfolds in three pivotal research topics: 1) The formulation and analysis of a game-theoretic decision-making model for energy retailers’ strategic bidding and offering in both wholesale and local energy markets while considering customers’ switching behaviour; 2) The introduction of a customised multi-energy pricing scheme, which is formulated as a bilevel optimisation model. The proposed model not only maximises the profit of energy retailers but also considers the multi-energy interdependencies and the diverse characteristics of microgrids; 3) The development of an innovative forecasting model named Patchformer, based on Transformer-based architectures and patch embedding method, for the prediction of long-term multienergy loads. This model improves forecasting accuracy, which enables a more reliable and efficient energy system management by predicting energy demands with high precision. This work presents a comprehensive approach to improving the effectiveness of transactive energy systems by merging advanced modelling techniques and machine/ deep learning models. This thesis tackles the current challenges in the field of transaction energy systems while also providing information for future research that aims to unlock the full potential of smart energy management in smart grids.
... In the proposed model, the uncertainty characteristics of PV units' output power, EV owners' behaviours, outside temperature of the customers' buildings, required energy of the RLs as well as the transmission network's requested flexibility capability are taken into account by applying an adaptive robust approach. In order to model a large EV fleet, aggregated model of the EVs at each bus is considered as a virtual battery (VB) at the corresponding bus and the VBs' characteristics are determined based on the EVs' uncertain behaviours [31]. Therefore, energy capacity and available power of the VBs are calculated based on the EV owners' driving pattern and have uncertainty. ...
... Moreover, S r,t VB_arr is defined as the amount of increase in the VBs' SoC at each time interval due to arrival of the EVs at the proposed time interval. In addition, S r,t VB_dep is introduced as the amount of decrease in the VBs' SoC at each time interval due to the EVs' departure [31]. Constraint (13) ensures that the VBs' charging and discharging operations do not occur at the same time by using binary variables x r,t ...
... SoC of the VBs should be within the limits at each time considering (17). Moreover, the VBs' SoC at the last time interval of the scheduling horizon is considered to be equal to the SoC at the beginning time interval [31]. ...
This paper presents a two‐stage adaptive robust optimization framework for day‐ahead energy and intra‐day flexibility self‐scheduling of a technical virtual power plant (TVPP). The TVPP exploits diverse distributed energy resources’ (DERs) flexibility capabilities in order to offer flexibility services to wholesale flexibility market as well as preserving the distribution network's operational constraints in the presence of DER uncertainties. The TVPP aims at maximizing its profit in energy and flexibility markets considering the worst‐case uncertainty realization. In the proposed framework, the first stage models the TVPP's participation strategy in day‐ahead energy market and determines the DERs’ optimal energy dispatch. The second stage addresses the TVPP's strategy in intra‐day flexibility market to determine the DERs’ optimal flexibility capability provision by adjusting their energy dispatch for the worst‐case realization of uncertainties. The uncertainty characteristics associated with photovoltaic units, electric vehicles, heating, ventilation and air conditioning systems, and other responsive loads as well as the transmission network's flexibility capability requests are considered using an adaptive robust approach. Adopting the duality theory, the model is formulated as a mixed‐integer linear programming problem and is solved using a column‐and‐constraint generation algorithm. This model is implemented on a standard test system and the model effectiveness is demonstrated.
... A quadratic programming technique was used to calculate the Nash equilibrium of the game model. Furthermore, González Vayá and Andersson [29] proposed a bidding price market with a bilevel mixed-integer linear programming approach, while Vandael et al. [30] proposed a reinforcement learning approach to learn and adapt to the charging behaviour of an EV fleet. Kristoffersen et al. [31] modelled EVs as prosumers that determined charging price through participation in a transitive market. ...
... Quirós-Tortós et al. [26] created probability density functions considering variables that were observed among all users, while in the proposed data augmentation module, new samples were generated based on each of the clusters found in the demand-profiling stage. In the demand-forecasting module, we treated the prediction as a multivariate regression problem, which was relative to the related work [28,29]. Going beyond prediction, we input this into the charge optimisation module for the EV charge scheduling. ...
... Going beyond prediction, we input this into the charge optimisation module for the EV charge scheduling. Although we solved this as an optimisation problem, González Vayá and Andersson [29] and Vandael et al. [30] framed scheduling as a bidding price market outcome and a reinforcement-based learning and adaptation method, respectively. ...
Electric vehicles (EVs) are advancing the transport sector towards a robust and reliable carbon-neutral future. Given this increasing uptake of EVs, electrical grids and power networks are faced with the challenges of distributed energy resources, specifically the charge and discharge requirements of the electric vehicle infrastructure (EVI). Simultaneously, the rapid digitalisation of electrical grids and EVs has led to the generation of large volumes of data on the supply, distribution and consumption of energy. Artificial intelligence (AI) algorithms can be leveraged to draw insights and decisions from these datasets. Despite several recent work in this space, a comprehensive study of the practical value of AI in charge-demand profiling, data augmentation, demand forecasting, demand explainability and charge optimisation of the EVI has not been formally investigated. The objective of this study was to design, develop and evaluate a comprehensive AI framework that addresses this gap in EVI. Results from the empirical evaluation of this AI framework on a real-world EVI case study confirm its contribution towards addressing the emerging challenges of distributed energy resources in EV adoption.
... Vayà et. al. [23] adopt chance-constrained programming to model uncertain PEV driving behaviors. Yao et. ...
... al. [24] use the CVaR to describe the regulation revenue considering both uncertainties in PEV behaviors and in regulation prices. However, references [23], [24] do not model the risks associated with penalties from poor regulation service delivery. These papers also both use scenario-based approximations, introducing the limitations described above. ...
This paper studies the behavior of a strategic aggregator offering regulation capacity on behalf of a group of distributed energy resources (DERs, e.g. plug-in electric vehicles) in a power market. Our objective is to maximize the aggregator's revenue while controlling the risk of penalties due to poor service delivery. To achieve this goal, we propose data-driven risk-averse strategies to effectively handle uncertainties in: 1) The DER parameters (e.g., load demands and flexibilities) and 2) sub-hourly regulation signals (to the accuracy of every few seconds). We design both the day-ahead and the hour-ahead strategies. In the day-ahead model, we develop a two-stage stochastic program to roughly model the above uncertainties, which achieves computational efficiency by leveraging novel aggregate models of both DER parameters and sub-hourly regulation signals. In the hour-ahead model, we formulate a data-driven distributionally robust chance-constrained program to explicitly model the aforementioned uncertainties. This program can effectively control the quality of regulation service based on the aggregator's risk aversion. Furthermore, it learns the distributions of the uncertain parameters from empirical data so that it outperforms existing techniques, (e.g. robust optimization or traditional chance-constrained programming) in both modelling accuracy and cost of robustness. Finally, we derive a conic safe approximation for it which can be efficiently solved by commercial solvers. Numerical experiments are conducted to validate the proposed method.
... However, the direct approach requires guaranteeing the stability of the proposed feedback law which can become a complex task if the system is nonlinear or if the objective function of the problem is non-Wherefore, considering the features and drawbacks of the direct control approach, this paper uses the hierarchical scheme to calculate the optimal charging/discharging decisions for the operator of a shared EVs fleet (also called aggregator) who participates in the electricity sales and ancillary services provision markets. More specifically, this second level is in charge of the EVs power and energy re-scheduling, adapting them to the new real conditions of the system, which can deviate from the mean expected conditions, considered at the first control level [8]. ...
... A complete dynamic description of the aforementioned car-sharing system model is given in the previous work [25], which corresponds with an aggregated energy model. This aggregated energy model is similar to the one proposed in [8]. However, the model used in [25] and in the current paper includes the features of the ancillary and car-sharing transport service provision. ...
This paper proposes a risk-aware control approach intended to generate the most profitable decisions for the manager of a public fleet of electric vehicles that can interact bidirectionally with the electrical network, providing different energy services to it. Specifically, the proposed control approach is intended to generate the best charging/discharging decisions for the fleet, including car-sharing uncertainties and the desired confidence level at which the fleet operator wants to cover these uncertainties. It considers a hierarchical control structure at whose first level an economic dynamic optimization is executed, and, at whose second level, a risk-aware reference tracking of the first-level references is performed. Using this a stochastic MPC controller at the second level, whose mathematical approach has as a novelty that it extends the current methodologies in the state of the art, allowing the inclusion of the linear time-varying behavior of the dynamic system, whose constraints are also time-varying, and whose uncertainties are additive-multiplicative with non-zero mean and non-unitary variance. Finally, the approach established is tested in a hypothetical car-sharing system located in Colombia.
... Lilliu et al. [34] and Bessa et al. [40] assumed unverified probability density functions for different aggregation parameter values when scheduling an EV fleet. Similarly, González Vayá and Andersson [41] added noise around the actual parameter values in their model. Yan et al. [42] considered simplified scenarios to account for uncertainty. ...
... As proposed by [41,44], the aggregated charging demand of an EV fleet can be optimized by modeling it as a virtual battery. Using this method, the characteristics of a set of charging sessions are reduced to a set of three virtual battery parameters for each timestep: the minimum and maximum aggregated charging energy since the beginning of the assessment timeframe (E min &E max , respectively) and the maximum aggregated charging power (P max ). ...
... Adaptive modifed (AM)-PSO is applied for MG operation in the presence of MT/FC/battery hybrid power source with aim of reducing cost and emission [10]. A bilevel EV aggregator bidding strategy framework is discussed, in which the upper-level goal is the reduction of EV charging costs and the lower-level issue is the maximization of social welfare [11]. Network limitations are not taken into account, and it is also supposed that all resources like generation, EV aggregators, and loads are placed at a single bus. ...
Nowadays, as the demand for plug-in electric vehicles in microgrids is growing, there are various challenges that the network must face, including providing adequate electricity, addressing environmental concerns, and rescheduling the microgrid. In order to overcome these challenges, this paper introduces a novel multiobjective optimization model where the first objective is to minimize the total operation cost of the microgrid and the second objective is to maximize the reliability index by reducing the amount of system energy not supplied. Because of these two compromising objectives, the evolutionary multiobjective seagull optimization algorithm is utilized to find the best local solutions. In this regard, integrated plug-in electric vehicles and demand response programs are used to smooth distribution locational marginal pricing. Furthermore, the effect of the system’s various configurations is analyzed in the suggested method to smooth the amount of distribution locational marginal prices in comparison to the initial case. Two case studies including modified IEEE 33-bus and 69-bus distribution networks are applied to evaluate the efficiency of the proposed approach.
... The literature [18] develops a two-tiered framework enabling demandside resource aggregators to engage in electricity trading, with the upper tier aiming to maximize social benefits and the lower tier aiming to maximize aggregator revenue. The literature [19] proposes a decision model for EV aggregators to take part in the day-ahead electricity market in response to the uncertainty of EV travel behavior. The literature [20] proposes an optimization method for aggregators to participate in market trading decisions, considering user interests and price elasticity coefficients. ...
In the context of a high proportion of new energy grid connections, demand-side resources have become an inevitable choice for constructing new power systems due to their high flexibility and fast response speed. However, the response capability of demand-side resources is decentralized and fluctuating, which makes it difficult for them to effectively participate in power market trading. Therefore, this paper proposes a robust transaction decision model for demand-side resource aggregators considering multi-objective clustering aggregation optimization. First, a demand-side resource aggregation operation model is designed to aggregate dispersed demand-side resources into a coordinated aggregated response entity through an aggregator. Second, the demand-side resource aggregation evaluation indexes are established from three dimensions of response capacity, response reliability, and response flexibility, and the multi-objective aggregation optimization model of demand-side resources is constructed with the objective function of the larger potential market revenue and the smallest risk of deviation penalty. Finally, robust optimization theory is adopted to cope with the uncertainty of demand-side resource responsiveness, the robust transaction decision model of demand-side resource aggregator is constructed, and a community in Henan Province is selected for simulation analysis to verify the validity and applicability of the proposed model. The findings reveal that the proposed cluster aggregation optimization method reduces the bias penalty risk of the demand-side resource aggregators by about 33.12%, improves the comprehensive optimization objective by about 18.10%, and realizes the optimal aggregation of demand-side resources that takes into account both economy and risk. Moreover, the robust trading decision model can increase the expected net revenue by about 3.1% under the ‘worst’ scenario of fluctuating uncertainties, which enhances the resilience of demand-side resource aggregators to risks and effectively fosters the involvement of demand-side resources in the electricity market dynamics.
... The authors in [11], [12] study the problem subjected to power flow constraints. In [13], the authors study the interactions of an EV aggregator and its EVs when providing frequency regulation. ...
An important function of aggregators is to enable the participation of small energy storage units in electricity markets. This paper studies two generally overlooked aspects related to aggregators of energy storage: i) the relationship between the aggregator and its constituent storage units and ii) the aggregator's effect on system welfare. Regarding i), we show that short-term outcomes can be Pareto-inefficient: all players could be better-off. In practice, however, aggregators and storage units are likely to engage in long rather than short-term relationships. Using Nash Bargaining Theory, we show that aggregators and storage units are likely to cooperate in the long-term. A rigorous understanding of the aggregator-storage unit relationship is fundamental to model the aggregator's participation in the market. Regarding ii), we first show that a profit-seeking energy storage aggregator is always beneficial to the system when compared to a system without storage, regardless of size or market power the aggregator may have. However, due to market power, a monopolist aggregator may act in a socially suboptimal manner. We propose a pricing scheme designed to mitigate market power abuse by the aggregator. This pricing scheme has several important characteristics: its formulation requires no private information, it incentivizes a rational aggregator to behave in a socially optimal manner, and allows for regulation of the aggregator's profit.
... The second step of the run-time market operation is characterized by the determination of backup deployment for multiple realizations of the indeterminate parameters. Energy and backup capacity can be co-optimized in the dayahead marketplace to give the system the flexibility to handle sporadic renewable electricity and potential deviations in the predicted demand [54], [55], [56]. ...
Conventional centralized optimization and management approaches may not work well in an emerging and distributed energy system with a high penetration of electric vehicles and green energy sources. The usage of blockchain technology is growing as a strong competitor as it can provide this kind of market with a transparent, secure, and efficient transactional platform. Nevertheless, most energy systems usually depend on complex mathematical optimization, which is poorly incorporated into blockchain applications. Moreover, time-sensitive message dissemination requirements, resource-intensiveness, high computational load, and communication overhead of the traditional blockchain consensus mechanisms make it difficult to connect with real-time vehicular networks. Here, we employ Proof of Intelligence (PoI), a novel prosumer-centric blockchain consensus mechanism to develop a comprehensive model of trust based on commitments of supply and demand through the application of peer-to-peer energy exchange with effective and dynamic integration of renewable sources and electric vehicles both in the day ahead and real-time energy trading platforms. Additionally, the PoI smart contract is developed to seamlessly incorporate mathematical optimization with an increased level of security, scalability, throughput, and low confirmation latency of transactions achieved through the reduced effort involved in finding and confirming the optimal solution in comparison with conventional blockchain consensus mechanisms.
... Furthermore, the application of tariff-based DR strategies has been explored in various contexts. For distribution network (Lu et al., 2018), optimal bidding strategy in day-ahead electricity markets (Vayá and Andersson, 2014), optimal pricing strategy in 5 pool-based electricity markets (Ruiz and Conejo, 2009), modeling coordinated cyberphysical attacks (Li et al., 2015), and optimal charging schedules of plug-in electric vehicles (Momber et al., 2015), among others. Summarizing the benefits of DR programs from the perspective of various electricity market participants is a complex task, given the extensive range of literature on the subject. ...
Demand response (DR) programs play a crucial role in improving system reliability and mitigating price volatility by altering the core profile of electricity consumption. This paper proposes a game-theoretical model that captures the dynamic interplay between retailers (leaders) and consumers (followers) in a tariffs-based electricity market under uncertainty. The proposed procedure offers theoretical and economic insights by analyzing demand flexibility within a hierarchical decision-making framework. In particular, two main market configurations are examined under uncertainty: i) there exists a retailer that exercises market power over consumers, and ii) the retailer and the consumers participate in a perfect competitive game. The former case is formulated as a mathematical program with equilibrium constraints (MPEC), whereas the latter case is recast as a mixed-integer linear program (MILP). These problems are solved by deriving equivalent tractable reformulations based on the Karush-Kuhn-Tucker (KKT) optimality conditions of each agent's problem. Numerical simulations based on real data from the European Energy Exchange platform are used to illustrate the performance of the proposed methodology. The results indicate that the proposed model effectively characterizes the interactions between retailers and flexible consumers in both perfect and imperfect market structures. Under perfect competition, the economic benefits extend not only to consumers but also to overall social welfare. Conversely, in an imperfect market, retailers leverage consumer flexibility to enhance their expected profits, transferring the risk of uncertainty to end-users. Additionally, the degree of consumer flexibility and their valuation of electricity consumption play significant roles in shaping market outcomes.
... In this situation, such demand-side players are considered as price-makers, whose bidding decisions have non-negligible influences on the market clearing price [4]. However, when their market behaviors cannot significantly affect the clearing price, they can be regarded as price-takers [5], and the clearing prices can be treated as exogenous variables [6]. Through exercising market power, price-maker LSEs have more arbitrage opportunities than price-taker LSEs to maximize profits [7]. ...
Deep reinforcement learning (DRL)-based methods have been widely used to learn optimal bidding and/or pricing strategies of load serving entities (LSEs) in electricity markets. However, previous studies on joint bidding and pricing (JBP) problem have been limited to model-based methods for price-maker LSEs or model-free methods for price-taker LSEs. In the context of addressing this research gap, this paper explores for the very first time a model-free multi-agent reinforcement learning (MARL)-based approach for the price-maker JBP problem. The original problem is first formulated as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP), where multiple agents are trained to find the optimal joint strategy in a fully cooperative setting. In order to overcome the challenges such as credit assignment and coordination, this paper proposes a parallelizable deep MT-MARL framework by incorporating multi-task learning (MTL) with MARL. Furthermore, an easily implementable multi-task multi-agent (MTMA) version of soft actor-critic (SAC), named as MTMA-SAC, is proposed to solve the Dec-POMDP efficiently based on the deep MT-MARL framework. The effectiveness, superiority and scalability of the proposed method is validated by numerical studies on systems of different scales. Case studies provide insightful analysis of some interesting characteristics caused by price-maker and line congestion.
... Given the urgent demand for real-time optimization to reduce EV charging costs, depending solely on programming-based methodologies might prove impractical [16]. To tackle this challenge, various day-ahead scheduling methods have been proposed [17][18][19][20][21][22][23]. These day-ahead scheduling methods aim to minimize the impact of an uncertain environment on EVs by employing robust or stochastic optimization in a day-ahead scenario. ...
Electric vehicle (EV) charging management combines forecasting, pricing, and scheduling, with pricing and forecasts significantly influencing scheduling models. AI-based forecasting frequently uses LSTM and GRU to handle complex dependencies, but attention-based mechanisms excel at capturing long-term dependencies. Managing EV charging is difficult due to limited battery capacity and unpredictable variables such as traffic, user behaviour, and electricity prices. Researchers prefer model-free approaches incorporating deep reinforcement learning (DRL) to address these challenges. This paper describes a DRL-based solution for optimizing in-home EV charging, presented as a Markov decision process (MDP). We present the novel modified GRU (MGRU) model, which builds on GRU, and an advanced model, the multi-head attention-based bi-directional MGRU (“MHA-BiMGRU”). This innovative model optimizes EV charging by utilizing historical energy prices and automatically scheduling actions based on real-time electricity pricing to meet user needs and reduce charging costs. The extensive simulations with several other variants of the proposed model and related RNN-based models conclusively confirm the effectiveness of the proposed model “MHA-BiMGRU”compared to conventional RNN-based models (LSTM and JANET) in increasing user satisfaction and significantly lowering EV owners’ charging expenses. Additionally, the proposed model reduces charging costs by 13% and 26% when using DQN and 15% and 36% when using DDPG, compared to the related LSTM and JANET-based models, respectively.
... [31] introduced the concept of two agent modes, the centralized protocol management mode (CPMM) and the decentralized demand response mode (DDRM), and developed a stochastic optimization model to maximize the expected profits of the EV aggregator. [32] focused on the problem of an aggregator bidding into the dayahead electricity market, with the former proposing a strategy to minimize charging costs while satisfying the flexible demand for plug-in electric vehicles (PEVs). Both studies highlight the potential cost reductions and the importance of flexible charging. ...
With the increasing adoption of electric vehicles (EVs), optimizing charging operations has become imperative to ensure efficient and sustainable mobility. This study proposes an optimization model for the charging and routing of electric vehicles between OD (Origin-Destination) demands. The objective is to develop an efficient and reliable charging plan that ensures the successful completion of trips while considering the limited range and charging requirements of electric vehicles. This paper presents an integrated model for optimizing electric vehicle (EV) charging operations, considering additional factors of setup time, charging time, bidding price estimation, and power availability from three sources: the electricity grid, solar energy, and wind energy. One crucial aspect addressed by the model is the estimation of bidding prices for both day-ahead and intra-day electricity markets. The model also considers the total power availability from the electricity grid, solar energy, and wind energy. The alignment of charging operations with the capacity of the grid and prevailing bidding prices is essential. This ensures that the charging process is optimized and can effectively adapt to the available grid capacity and market conditions. The utilization of renewable energies led to a 42% decrease in the electricity storage capacity available in batteries at charging stations. Furthermore, this integration leads to a substantial cost reduction of approximately 69% compared to scenarios where renewable energy is not utilized. Hence, the proposed model can design renewable energy systems based on the required electricity capacity at charging stations. These findings highlight the compelling financial advantages associated with the adoption of sustainable power sources.
... Discharge volume, reservoir content, and spillage from station n are limited in (13). ...
With the increasing integration of power plants into the frequency-regulation markets, the importance of optimal trading has grown substantially. This paper conducts an in-depth analysis of their optimal trading behavior in sequential day-ahead, intraday, and frequency-regulation markets. We introduce a probabilistic multi-product optimization model, derived through a series of transformation techniques. Additionally, we present two reformulations that re-frame the problem as a mixed-integer linear programming problem with uncertain parameters. Various aspects of the model are thoroughly examined to observe the optimal multi-product trading behavior of hydro power plant assets, along with numerous case studies. Leveraging historical data from Nordic electricity markets, we construct realistic scenarios for the uncertain parameters. Furthermore, we then proposed an algorithm based on the No-U-Turn sampler to provide probability distribution functions of cleared prices in frequency-regulation and day-ahead markets. These distribution functions offer valuable statistical insights into temporal price risks for informed multi-product optimal-trading decisions.
... A critical challenge in implementing the hierarchical dispatch is accurately representing the feasible charging region for multiple EVs. To address this, researchers have developed virtual battery models, treating the EV fleet as a single entity with dynamic power and energy limits [8], [9], [10], [11], [12]. The Cumulative Energy and Power Boundaries (CEPB) model [8], [9], [13] is a notable example, using the slowest and fastest charging trajectories to approximate the aggregated feasible region. ...
This paper presents a novel method for Electric Vehicle Aggregators (EVAs) to engage in day-ahead and real-time electricity markets, overcoming the issue of cross-day energy gaps of EVAs. Traditional day-ahead bidding processes are usually treated as independent, one-time actions, and fail to consider the continuity of the cross-day energy status of the EV fleet. To tackle this problem, the Endpoint Energy and Power Boundary (EEPB) model is introduced, which is achieved by decomposing each EV charging event into multiple events based on the split points. Then, a two-layer method to determine the optimal split points, i.e., split times and split energy levels, which minimizes the flexibility loss of the EVA, is proposed. Additionally, a novel cross-day bidding method for EVAs, which aligns with the day-by-day bidding process, is proposed. This method utilizes EEPB based on historical EV charging records, restores flexibility on the operating day, and implements a risk-averse cross-day bidding strategy. These proposed methods are tested on about 700,000 real-world residential EV charging records in North China between 2021 and 2022, demonstrating their effectiveness in addressing cross-day energy gaps, reducing charging flexibility losses, and achieving a balance between cost and risk for EVAs.
... Notably, the IEEE Transactions on Power Systems ranks as the top journal in terms of the highest average number of citations. This recognition is due to the 2015 publication [147] by Gonzalez Vaya M. This paper presents an optimal bidding strategy for a plug-in electric vehicle aggregator in day-ahead electricity markets under uncertainty. ...
The landscape of Demand-Side Energy Management (DSM) research is rapidly evolving, shaped by technological innovations and policy developments. This paper presents an exhaustive bibliometric analysis and methodological framework to explore the research trends within the DSM domain. By synthesizing data from Scopus and OpenAlex, we compile a comprehensive dataset of DSM publications that serve as the basis for our analysis. Through rigorous data acquisition and cleaning, we ensure the reliability and relevance of our dataset. We employ state-of-the-art Large Language Models (LLMs) and topic modeling techniques, including GPT and BERTopic, to perform semantic analysis and uncover thematic structures within the literature. Statistical analysis of the literature dataset reveals a steady increase in DSM publications, with significant contributions from prestigious journals and institutions worldwide. We observe that articles are the predominant publication type, while reviews often cite more references and receive higher citation counts. The distribution of publications over time indicates a growing interest in DSM, particularly since 2014. Geographical mapping of institutions highlights key regions contributing to DSM research, with notable outputs from Europe, North America, and East Asia. Coupled with citation network analysis, our approach reveals the influential works and emerging trends that define the scientific progression of DSM research. Our unsupervised topic modeling, powered by BERTopic, clusters the publications into distinct themes, while our advanced visualiza-tion techniques using UMAP and t-SNE provide insights into the semantic space of DSM literature. The resulting thematic classification is presented in a hierarchical structure, offering a comprehensive understanding of the field's focus areas. Our citation network analysis, utilizing force-directed graph computation and edge-bundling algorithms, maps the interconnectivity and impact of research contributions, providing a dynamic view of the field's evolution. This study not only charts the landscape of DSM research but also offers a methodological blueprint for future biblio-metric analyses. The insights gained from this multi-faceted exploration serve as a valuable resource for researchers, policymakers, and industry practitioners looking to navigate the complexities of DSM and contribute to its scientific advancement.
... In this context, the role of intermediaries might support the pooling of distributed flexibilities from PEV charging (Niesten and Alkemade, 2016;Ringler et al., 2016). Demand response provides a perfect opportunity for PEV aggregation agents to use smart charging to reduce costs (Gonzalez Vaya and Andersson, 2015) and, therefore, increase aggregator profits (Shafie-khah et al., 2016). Several case studies support this result for different regions of the world: Schill (2011) studies the effect of PEV on an imperfectly competitive German electricity market and shows that consumers benefit from PEV if excess battery capacity can be used for grid storage. ...
Growing numbers of plug-in electric vehicles in Europe will have an increasing impact on the electricity system. Using the agent-based simulation model PowerACE for ten electricity markets in Central Europe, we analyze how different charging strategies impact price levels and production- as well as consumption-based carbon emissions in France and Germany. The applied smart charging strategies consider spot market prices and/or real-time production from renewable energy sources. While total European carbon emissions do not change significantly in response to the charging strategy due to the comparatively small energy consumption of the electric vehicle fleet, our results show that all smart charging strategies reduce price levels on the spot market and lower total curtailment of renewables. Here, charging processes optimized according to hourly prices have the strongest effect. Furthermore, smart charging strategies reduce electricity purchasing costs for aggregators by about 10% compared to uncontrolled charging. In addition, the strategies allow aggregators to communicate near-zero allocated emissions for charging vehicles. An aggregator’s charging strategy expanding classic electricity cost minimization by limiting total national PEV demand to 10% of available electricity production from renewable energy sources leads to the most favorable results in both metrics, purchasing costs and allocated emissions. Finally, aggregators and plug-in electric vehicle owners would benefit from the availability of national, real-time Guarantees of Origin and the respective scarcity signals for renewable production.
... Currently, subscription with only one DR-aggregator is allowed by the wholesale electricity markets [8], although the literature has considered alternative future scenaria [9]. Existing frameworks consider the optimization of either the DR-aggregator's [10] or the prosumer's [11] cost, but not both, therefore neglecting the competition between them. Various pricing schemes and prosumer devices have been considered [12]- [14]. ...
In this work, a Stackelberg game theoretic framework is proposed for trading energy bidirectionally between the demand-response (DR) aggregator and the prosumers. This formulation allows for flexible energy arbitrage and additional monetary rewards while ensuring that the prosumers' desired daily energy demand is met. Then, a scalable (with the number of prosumers) approach is proposed to find approximate equilibria based on online sampling and learning of the prosumers' cumulative best response. Moreover, bounds are provided on the quality of the approximate equilibrium solution. Last, real-world data from the California day-ahead energy market and the University of California at Davis building energy demands are utilized to demonstrate the efficacy of the proposed framework and the online scalable solution.
... To cope with those issues, there has been a number of works on the stochasticity of electricity markets. One focus of such works were mostly on modeling of uncertainties caused by intermittent renewables, e.g., [15][16][17][18][19]; DERs, e.g., electric vehicles [20][21][22]; and uncertain behaviors of market participants [23]. Other focuses are on the characterization of market clearing solutions under above circumstances, and mechanisms for distributing revenues and recovering costs, e.g., [24,25]. ...
This paper investigates stochastic market clearing solutions in peer-to-peer (P2P) energy markets as two parameters of prosumers' cost functions are flexibly and randomly chosen in certain intervals to compensate uncertainties and guarantee their energy preferences. For tractability, the scenarios in which one parameter is varied while the other is fixed are considered. It is then shown that a few distributions, namely normal, Cauchy, and gamma distributions, are invariant for P2P energy markets, i.e., if prosumer cost function parameters follow one of those distributions, then market clearing solutions will follow the same type of distribution. Explicit and analytical formulas for probability density functions of market clearing price and trading powers in P2P energy markets are derived for each type of the above-mentioned distributions. As such, variations on P2P energy market clearing solutions can be assessed and predicted when parameters in the stochastic distribution of prosumer cost function parameters are changed. Simulations are carried out for a modified IEEE European Low Voltage Test Feeder system whose results validate the obtained theoretical characterizations.
... With the advancement of commercial nonlinear solvers, it is also possible to solve MPEC problems without applying linearization techniques (Artelys Knitro, 2021). On a different front and considering their specific objectives, DR models have been used for different power system applications such as developing DR dynamic pricing in a distribution network (Lu et al., 2018), optimal bidding strategy in day-ahead electricity markets (Vayá and Andersson, 2014), optimal pricing strategy in pool-based electricity markets (Ruiz and Conejo, 2009), modeling coordinated cyber-physical attacks (Li et al., 2015), and optimal charging schedules of plug-in electric vehicles (Momber et al., 2015), among others. However, much of the literature on dynamic electricity price tariffs has focused on the impact on individual energy markets players such as demand aggregator cost minimization, distribution system operators (DSO) optimal reconfiguration of microgrid, transmission system operators (TSO) optimal allocation of distributed generations, retailer profit or consumer benefit (Ajoulabadi et al., 2020;Wang et al., 2020a;Nejad et al., 2019;Dagoumas and Polemis, 2017;Nilsson et al., 2018). ...
Demand response (DR) programs have gained much attention after the restructuring of the electricity markets and have been used to optimize the decisions of market participants. They can potentially enhance system reliability and manage price volatility by modifying the amount or time of electricity consumption. This paper proposes a novel game-theoretical model accounting for the relationship between retailers (leaders) and consumers (followers) in a dynamic price environment under uncertainty. The quality and economic gains brought by the proposed procedure essentially stem from the utilization of demand elasticity in a hierarchical decision process that renders the options of different market configurations under different sources of uncertainty. The model is solved under two frameworks: by considering the retailer's market power and by accounting for an equilibrium setting based on a perfect competitive game. These are formulated in terms of a mathematical program with equilibrium constraints (MPEC) and with a mixed-integer linear program (MILP), respectively. In particular, the retailers' market power model is first formulated as a bi-level optimization problem, and the MPEC is subsequently derived by replacing the consumers' problem (lower level) with its Karush-Kuhn-Tucker (KKT) optimality conditions. In contrast, the equilibrium model is solved as a MILP by concatenating the retailer's and consumers' KKT optimality conditions. We illustrate the proposed procedure and numerically assess the performance of the model using realistic data. Numerical results show the applicability and effectiveness of the proposed model to explore the interactions of market power and DR programs. The results confirm that consumers are better off in an equilibrium framework while the retailer increases its expected profit when exercising its market power.
... Nowadays, market power is changing along with the development of the electricity market. In particular, the increasing penetration of renewable energy resources (RESs) in recent years [7], [8], emerging market participants (such as electric vehicle (EV), storage, etc.) [9], [10] and flexible demand response sources (DRSs) have brought variability and uncertainty to power systems, reshaping the way in which electricity markets operate [11] - [13]. Another significant change is the reconstitution of the electricity market during the transition to a low-carbon power system, which has raised new requirements for market design and regulations [14]. ...
The deregulation of the power industry requires avoiding market power abuse to maintain the market competitiveness. To this end, a sequence of assessment measurements or mitigation mechanisms is required. Meanwhile, the increasing renewable energy resources (RESs) and flexible demand response resources (DRSs) are changing the behaviors of market participants and creating new cases of market power abuse. Such new circumstances bring the new evaluation and control methods of market power to the forefront. This paper provides a comprehensive review of market power in the reshaping of power systems due to the increasing RES and the development of DRS. The market power at the supply side, demand side, and in the multi-energy system is categorized and reviewed. In addition, the applications of market power supervision measures in the US, the Nordics, UK, and China are summarized. Furthermore, the unsolved issues, possible key technologies, and potential research topics on market power are discussed.
... The PDF of the objective function is evaluated against criteria and constraints. For example, the point estimate method (PEM) can be utilised to construct different PDFs [35,88]. To handle the uncertainty problem, the VPP coordinates DRs, energy production and storage units to reduce the total risks for the VPP and to maximise the VPP's profit. ...
Virtual Power Plants (VPPs) are efficient structures for attracting private investment, increasing the penetration of renewable energy and reducing the cost of electricity for consumers. It is expected that the number of VPPs will increase rapidly as their financial return is attractive to investors. VPPs will provide added value to consumers, to power systems and to electricity markets by contributing to different services such as the energy and load-following services. One of the capabilities that will become critical in the near future, when large power plants are retired, is grid-forming capability. This review paper introduces the concept of grid-forming VPPs along with their corresponding technologies and their advantages for the new generation of power systems with many connected VPPs.
... In addition, the lower level handles the market clearing process, where the bidding of other participants of the market is not based on the aggregator's bidding behavior. Similar research has been accomplished in [52], where the optimal day-ahead bidding framework has been proposed for EVA to minimize the charging cost of EVs without considering the capability of V2G. The bi-level programming is introduced for studying the charging cost at the upper level and market clearing at the lower level. ...
Electric vehicles (EVs) are predicted to be highly integrated into future smart grids considering their significant role in achieving a safe environment and sustainable transportation. The charging/discharging flexibility of EVs, which can be aggregated by an agent, provides the opportunity of participating in the demand-side management of energy networks. The individual participation of consumers at the system level would not be possible for two main reasons: (i) In general, their individual capacity is below the required minimum to participate in power system markets, and (ii) the number of market participants would be large, and thus the volume of individual transactions would be difficult to manage. In order to facilitate the interactions between consumers and the power grid, an aggregation agent would be required. The EV aggregation area and their integration challenges and impacts on electricity markets and distribution networks is investigated in much research studies from different planning and operation points of view. This paper aims to provide a comprehensive review and outlook on EV aggregation models in electrical energy systems. The authors aim to study the main objectives and contributions of recent papers and investigate the proposed models in such areas in detail. In addition, this paper discusses the primary considerations and challenging issues of EV aggregators reported by various research studies. In addition, the proposed research outlines the future trends around electric vehicle aggregators and their role in electrical energy systems.
Demand response programs play a crucial role in improving system reliability and mitigating price volatility by altering the core profile of electricity consumption. This paper proposes a game-theoretical model that captures the dynamic interplay between retailers (leaders) and consumers (followers) in a tariff-based electricity market under uncertainty. The proposed procedure offers theoretical and economic insights by analyzing consumer flexibility within a hierarchical decision-making framework. In particular, two main market configurations are examined under uncertainty: i) there exists a retailer that exercises market power over consumers, and ii) the retailer and the consumers participate in a perfect competitive game. The former is formulated as a mathematical program with equilibrium constraints, whereas the latter is recast as a mixed-integer linear program. These problems are solved by deriving equivalent tractable reformulations based on the Karush-Kuhn-Tucker (KKT) optimality conditions of each agent’s problem. Numerical simulations based on real data from the European Energy Exchange (EEX) are used to illustrate the performance of the proposed methodology. The results indicate that the proposed model effectively characterizes the interactions between retailers and flexible consumers in both perfect and imperfect market structures. Under perfect competition, the economic benefits extend not only to consumers but also to overall social welfare. Conversely, in an imperfect market, retailers leverage consumer flexibility to enhance their expected profits, transferring the risk of uncertainty to end-users. Additionally, the degree of consumer flexibility and consumers’ valuation of electricity consumption play significant roles in shaping market outcomes. These findings highlight the crucial impact of market structure and consumer behavior on the dynamics of electricity market pricing under demand response programs.
Plug-in electric vehicles (PEVs) are considered as flexible loads since their charging schedules can be shifted over the course of a day without impacting drivers mobility. This property can be exploited to reduce charging costs and adverse network impacts. The increasing number of PEVs makes the use of distributed charging coordinating strategies preferable to centralized ones. In this paper, we propose an agent-based method which enables a fully distributed solution of the PEVs Coordinated Charging (PEV-CC) problem. This problem aims at coordinating the charging schedules of a fleet of PEVs to minimize costs of serving demand subject to individual PEV constraints originating from battery limitations and charging infrastructure characteristics. In our proposed approach, each PEVs charging station is considered as an agent that is equipped with communication and computation capabilities. Our multiagent approach is an iterative procedure which finds a distributed solution for the first order optimality conditions of the underlying optimization problem through local computations and limited information exchange with neighboring agents. In particular, the updates for each agent incorporate local information such as the Lagrange multipliers, as well as enforcing the local PEVs constraints as local innovation terms. Finally, the performance of our proposed algorithm is evaluated on a fleet of 100 PEVs as a test case, and the results are compared with the centralized solution of the PEV-CC problem.
In the pathway to 2030 electricity generation decarbonization and 2050 net-zero economies, scalable integration of distributed load can support environmental goals and also help alleviate smart grid operational issues through its electricity market participation. In this work, a novel Stackelberg game theoretic framework is proposed for trading the energy bidirectionally between the demand-response (DR) aggregator and the prosumers (distributed load). This formulation allows for flexible energy arbitrage and additional monetary rewards while ensuring that the prosumers’ desired daily energy demand is met. Then, a scalable (linear with the number of prosumers and the number of learning samples), the decentralized privacy-preserving algorithm is proposed to find approximate equilibria with online sampling and learning of the prosumers’ cumulative best response, which finds applications beyond this energy game. Moreover, cost bounds are provided on the quality of the approximate equilibrium solution. Finally, the real data from the California day-ahead market and the UC Davis campus building energy demands are utilized to demonstrate the efficacy of the proposed framework and the algorithm.
Growing numbers of plug-in electric vehicles in Europe will have an increasing impact on the electricity system. Using the agent-based simulation model PowerACE for ten electricity markets in Central Europe, we analyze how different charging strategies impact price levels and production- as well as consumption-based carbon emissions in France and Germany. The applied smart charging strategies consider spot market prices and/or real-time production from renewable energy sources.
While total European carbon emissions do not change significantly in response to the charging strategy due to the comparatively small energy consumption of the electric vehicle fleet, our results show that all smart charging strategies reduce price levels on the spot market and lower total curtailment of renewables. Here, charging processes optimized according to hourly prices have the strongest effect. Furthermore, smart charging strategies reduce electricity purchasing costs for aggregators by about 10% compared to uncontrolled charging. In addition, the strategies allow aggregators to communicate near-zero allocated emissions for charging vehicles. An aggregator's charging strategy expanding classic electricity cost minimization by limiting total national PEV demand to 10% of available electricity production from renewable energy sources leads to the most favorable results in both metrics, purchasing costs and allocated emissions. Finally, aggregators and plug-in electric vehicle owners would benefit from the availability of national, real-time Guarantees of Origin and the respective scarcity signals for renewable production.
In this chapter, the effects of several important parameters, such as plug-in electric vehicle (EV) type, EV penetration level, and driver’s social class, on the optimal charging management of EVs in San Francisco are studied in different sensitivity analyses. The objective function of the main problem is to minimize the operation cost of electrical distribution network penetrated by renewables, where a stochastic model predictive control (SMPC) is applied in the optimization technique to address the variability and uncertainty issues of EVs’ state of charge (SOC) and renewables’ power. Herein, quantum-inspired simulated annealing (QISA) algorithm is applied as the optimization technique. In this study, the drivers’ responsiveness probability to provide vehicle-to-grid (V2G) service at the parking lot is modeled with respect to the amount of incentive, the drivers’ social class, and the real driving routes of 100 vehicles in San Francisco.KeywordsDrivers’ social classDriving routes in San FranciscoElectric vehicle (EV)Quantum-inspired simulated annealing (QISA) algorithmRenewablesStochastic model predictive control (SMPC)
Aggregators, especially those with non-generating, energy-limited or uncertain resources, often have problems of not meeting the eligibility requirements of the electricity markets, which limits their opportunities to profit from the markets. To this end, this paper proposes a cooperated bidding model to help the unqualified aggregators to meet two main eligibility requirements, i.e., minimum power requirement and minimum duration time requirement. In specific, two cooperation models are built up in the pre-bidding process. An
add-up power constraint
is built to combine the small-power resources into a larger one, and a
pack-up model
is proposed to pack up the duration-unqualified resources into an equivalent qualified one. Then, the above models are embedded into the cooperated bidding optimization model. Two solving strategies, including a centralized optimization and a distributed optimization algorithm based on the alternating direction method of multipliers (ADMM), are developed. The latter one requires only the information of to-be-cooperated resources and interexchange power, which can better protect the private data of the cooperated aggregators. Case studies verify the feasibility and effectiveness of the proposed cooperation method.
The permeation of electric/hybrid-electric vehicles in the transportation sector will increase the transition of energy demand from fuel supply systems to grid-based systems for power support. Challenges in electrified transport ecosystem development are multifaceted. Today energy systems are developed for vehicles as independent and isolated units, nevertheless, the view of eco-cities as smart energy flow systems have opened new perspectives. In the future the overall energy demand has to be considered, relying on uncertain consumer behaviors, the eco-energy production generating from the city’s assets as well as non-renewable resources and storages allowing additional and smooth power flow. The dependency on production constraints has to be considered. Finally, energy transfer relies on various mechanisms to share energy within the overall system. To guide the energy flow towards demand satisfaction and grid stability it becomes obvious that information and communication technologies are needed. Through a systematic review and analysis, this paper aims to highlight the goals of smart city development from the perspective of electrical sources and sinks (mainly the transportation sector), the principal options to store and to control energy flows with respect to the power grid, the electric utilities, fleet operators, and electric vehicles. The paper formulates a multi-agent system problem to describe the role of the entities which are assumed as agents maximizing the renewable energy options and minimizing the energy losses. This can be utilized by considering scenarios of multiple agents working in co-ordination with each other and with the environment taking into account system interactions and constraints. || Link to our repository: https://www.uni-due.de/srs/veroeffentlichungen-srs.php?Jahr=alle
The electrification of transportation is seen as one of the solutions to challenges such as global warming, sustainability, and geopolitical concerns on the availability of oil. From the perspective of power systems, an introduction of plug‐in electric vehicles presents many challenges but also opportunities to the operation and planning of power systems. On the one hand, if vehicles are considered regular loads without flexibility, uncontrolled charging can lead to problems at different network levels endangering secure operation of installed assets. However, with direct or indirect control approaches the charging of vehicles can be managed in a desirable way, e.g., shifted to low‐load hours. Furthermore, vehicles can be used as distributed storage resources to contribute to ancillary services for the system, such as frequency regulation and peak‐shaving power or help integrate fluctuating renewable resources. All these modes of operation need appropriate regulatory frameworks and market design if the flexibility of the vehicles is to be capitalized. In most of the proposed approaches, a so‐called aggregator could be in charge of directly or indirectly controlling the charging of vehicles and serve as an interface with other entities such as the transmission system operator or energy service providers. However, fully decentralized schemes without an aggregator are also conceivable, for instance, to provide primary frequency control. Communication also plays a key role, as in most of the control schemes a significant amount of information needs to be transmitted between vehicles and control entities. The management of electric vehicles as distributed resources fits well in the paradigm of smart grids, where an advanced use of communication technologies and metering infrastructure, increased controllability and load flexibility, and a larger share of fluctuating and distributed resources are foreseen.
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