Publications
31 results found
Ye Y, Papadaskalopoulos D, Yuan Q, et al., 2023, Multi-Agent Deep Reinforcement Learning for Coordinated Energy Trading and Flexibility Services Provision in Local Electricity Markets, IEEE TRANSACTIONS ON SMART GRID, Vol: 14, Pages: 1541-1554, ISSN: 1949-3053
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- Citations: 6
Qiu D, Ye Y, Papadaskalopoulos D, 2020, Exploring the effects of local energy markets on electricity retailers and customers, Electric Power Systems Research, Vol: 189, ISSN: 0378-7796
Local energy markets (LEM) have recently attracted great interest as they enable effective coordination of small-scale distributed energy resources (DER) at the customer side, and avoidance of distribution network reinforcements. However, the introduction of LEM has also significant implications on the strategic interactions between the customers and incumbent electricity retailers. This paper explores for the first time these interactions by proposing a novel multi-period bi-level optimization model, which captures the pricing decisions of a strategic retailer in the upper level (UL) and the response of both independent customers and the LEM (both including flexible consumers, micro-generators and energy storages) in the lower level (LL). Since the LL problem representing the LEM is non-convex, a new analytical approach is employed for solving the developed bi-level problem. The examined case studies demonstrate that the introduction of an LEM reduces the customers’ energy dependency on the retailer and limits the retailer’s strategic potential of exploiting the customers through large differentials between buy and sell prices. As a result, the profit of the retailer is significantly reduced while the customers, primarily the LEM participants and to a lower extent non-participating customers, achieve significant economic benefits.
Ye Y, Qiu D, Sun M, et al., 2020, Deep reinforcement learning for strategic bidding in electricity markets, IEEE Transactions on Smart Grid, Vol: 11, Pages: 1343-1355, ISSN: 1949-3053
Bi-level optimization and reinforcement learning (RL) constitute the state-of-the-art frameworks for modeling strategic bidding decisions in deregulated electricity markets. However, the former neglects the market participants' physical non-convex operating characteristics, while conventional RL methods require discretization of state and / or action spaces and thus suffer from the curse of dimensionality. This paper proposes a novel deep reinforcement learning (DRL) based methodology, combining a deep deterministic policy gradient (DDPG) method with a prioritized experience replay (PER) strategy. This approach sets up the problem in multi-dimensional continuous state and action spaces, enabling market participants to receive accurate feedback regarding the impact of their bidding decisions on the market clearing outcome, and devise more profitable bidding decisions by exploiting the entire action domain, also accounting for the effect of non-convex operating characteristics. Case studies demonstrate that the proposed methodology achieves a significantly higher profit than the alternative state-of-the-art methods, and exhibits a more favourable computational performance than benchmark RL methods due to the employment of the PER strategy.
Ye Y, Papadaskalopoulos D, Kazempour J, et al., 2020, Incorporating non-convex operating characteristics into bi-level optimization electricity market models, IEEE Transactions on Power Systems, Vol: 35, Pages: 163-176, ISSN: 0885-8950
Bi-level optimization constitutes the most popular mathematical methodology for modeling the deregulated electricity market. However, state-of-the-art models neglect the physical non-convex operating characteristics of market participants, due to their inherent inability to capture binary decision variables in their representation of the market clearing process, rendering them problematic in modeling markets with complex bidding and unit commitment (UC) clearing mechanisms. This paper addresses this fundamental limitation by proposing a novel modeling approach enabling incorporation of these non-convexities into bi-level optimization market models, which is based on the relaxation and primal-dual reformulation of the original, non-convex lower level problem and the penalization of the associated duality gap. Case studies demonstrate the ability of the proposed approach to closely approximate the market clearing solution of the actual UC clearing algorithm and devise more profitable bidding decisions for strategic producers than the state-of-the-art bi-level optimization approach, and reveal the potential of strategic behavior in terms of misreporting non-convex operating characteristics.
Ye Y, Qiu D, Papadaskalopoulos D, et al., 2019, A deep Q network approach for optimizing offering strategies in electricity markets, 2019 International Conference on Smart Energy Systems and Technologies (SEST), Publisher: IEEE
Bi-level optimization constitutes the most common mathematical methodology for modeling the decision-making process of strategic generation companies in deregulated electricity markets. However, previous models neglect the physical non-convex operating characteristics of generation units, due to their inherent inability to capture binary decision variables in their representation of the market clearing process, rendering them problematic in the context of markets with complex biding and unit commitment clearing mechanisms. Aiming at addressing this fundamental limitation, this paper delves into deep reinforcement learning-based approaches by proposing a novel deep Q network (DQN) method and enabling explicit incorporation of these non-convexities into the bi-level optimization model. Case studies demonstrate the superior performance of the proposed method over the conventional Q-learning method in devising more profitable offering strategies.
Ye Y, Papadaskalopoulos D, Moreira R, et al., 2019, Investigating the impacts of price-taking and price-making energy storage in electricity markets through an equilibrium programming model, IET Generation, Transmission and Distribution, Vol: 13, Pages: 305-315, ISSN: 1350-2360
The envisaged decarbonisation of electricity systems has attracted significant interest around the role and value of energy storage systems (ESSs). In the deregulated electricity market, there is a need to investigate the complex impacts of ESSs, considering the potential exercise of market power by strategic players. This study aims at comprehensively analysing the impacts of both price-taking and price-making storage behaviours on energy market efficiency, corresponding to potential settings with small and large storage players, respectively. In order to achieve this and in contrast to previous papers, this work develops a multi-period equilibrium programming market model to determine market equilibrium stemming from the interactions of independent strategic producers and ESSs, while capturing the time-coupling operational constraints of ESSs as well as network constraints. The results of case studies on a test market capturing the general conditions of the GB electricity system demonstrate that the introduction of ESSs mitigates market power exercise and improves market efficiency, with this beneficial impact being higher when ESSs act as price takers. When the electricity network is congested, the location of ESSs also affects the market outcome, with their beneficial impact on market efficiency being higher when they are located in higher-priced areas.
Fatouros P, Konstantelos I, Papadaskalopoulos D, et al., 2019, Stochastic Dual Dynamic Programming for Operation of DER Aggregators Under Multi-Dimensional Uncertainty, IEEE Transactions on Sustainable Energy, Vol: 10, Pages: 459-469, ISSN: 1949-3029
The operation of aggregators of distributed energy resources (DER) is highly complex, since it entails the optimal coordination of a diverse portfolio of DER under multiple sources of uncertainty. The large number of possible stochastic realizations that arise, can lead to complex operational models that become problematic in real-time market environments. Previous stochastic programming approaches resort to two-stage uncertainty models and scenario reduction techniques to preserve the tractability of the problem. However, two-stage models cannot fully capture the evolution of uncertain processes and the a priori scenario selection can lead to suboptimal decisions. In this context, this paper develops a novel stochastic dual dynamic programming (SDDP) approach which does not require discretization of either the state space or the uncertain variables and can be efficiently applied to a multi-stage uncertainty model. Temporal dependencies of the uncertain variables as well as dependencies among different uncertain variables can be captured through the integration of any linear multidimensional stochastic model, and it is showcased for a p-order vector autoregressive (VAR) model. The proposed approach is compared against a traditional scenario-tree-based approach through a Monte-Carlo validation process, and is demonstrated to achieve a better trade-off between solution efficiency and computational effort.
Papadaskalopoulos D, Moreira R, Strbac G, et al., 2018, Quantifying the potential economic benefits of flexible industrial demand in the European power system, IEEE Transactions on Industrial Informatics, Vol: 14, Pages: 5123-5132, ISSN: 1551-3203
The envisaged decarbonization of the European power system introduces complex techno-economic challenges to its operation and development. Demand flexibility can significantly contribute in addressing these challenges and enable a cost-effective transition to the low-carbon future. Although extensive previous work has analyzed the impacts of residential and commercial demand flexibility, the respective potential of the industrial sector has not yet been thoroughly investigated despite its large size. This paper presents a novel, whole-system modeling framework to comprehensively quantify the potential economic benefits of flexible industrial demand (FID) for the European power system. This framework considers generation, transmission and distribution sectors of the system, and determines the least-cost long-term investment and short-term operation decisions. FID is represented through a generic, process-agnostic model, which however accounts for fixed energy requirements and load recovery effects associated with industrial processes. The numerical studies demonstrate multiple significant value streams of FID in Europe, including capital cost savings by avoiding investments in additional generation and transmission capacity and distribution reinforcements, as well as operating cost savings by enabling higher utilization of renewable generation sources and providing balancing services.
Pudjianto D, Papadaskalopoulos D, Moreira R, et al., 2018, Flexibility Potential of Industrial Electricity Demand: Insights from the H2020 IndustRE project, The 11th Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion
Oderinwale T, Papadaskalopoulos D, Ye Y, et al., 2018, Incorporating demand flexibility in strategic generation investment planning, 15th International Conference on the European Energy Market, Publisher: IEEE
The envisaged decarbonization of electricity systems has attracted significant interest around the role and value of demand flexibility.However, the impact of this flexibility on generation investments in the deregulated electricity industry setting remains a largely unexplored area, since previous relevant work neglectsthe time-coupling nature of demand shifting potentials.This paper addresses this challenge by proposing a strategic generation investment planning model expressing the decision making process of a self-interestedgeneration company and accounting for the time-coupling operational characteristics of demand flexibility. This model is formulated as a multi-period bi-level optimization problem, which is solved after converting it to a Mathematical Program with Equilibrium Constraints (MPEC).Case studies with the proposedmodel demonstrate that demand flexibility reduces the total generation capacity investment, enhances investments in baseload generation and yieldssignificant economic benefits in terms of total system costs and demand payments.
De Paola A, Papadaskalopoulos D, Angeli D, et al., 2018, Investigating the social efficiency of merchant transmission planning through a non-cooperative game-theoretic framework, IEEE Transactions on Power Systems, Vol: 33, Pages: 4831-4841, ISSN: 0885-8950
Merchant transmission planning is considered as a further step towards the full liberalization of the electricity industry. However, previous modeling approaches do not comprehensively explore its social efficiency as they cannot effectively deal with a large number of merchant companies. This paper addresses this fundamental challenge by adopting a novel non-cooperative game-theoretic approach. Specifically, the number of merchant companies is assumed sufficiently large to be approximated as a continuum. This allows the derivation of mathematical conditions for the existence of a Nash Equilibrium solution of the merchant planning game. By analytically and numerically comparing this solution against the one obtained through the traditional centralized planning approach, the paper demonstrates that merchant planning can maximize social welfare only when the following conditions are satisfied: a) fixed investment costs are neglected and b) the network is radial and does not include any loops. Given that these conditions do not generally hold in reality, these findings suggest that even a fully competitive merchant transmission planning framework, involving the participation of a very large number of competing merchant companies, is not generally capable of maximizing social welfare.
Ye Y, Papadaskalopoulos D, Strbac G, 2018, Investigating the ability of demand shifting to mitigate electricity producers’ market power, IEEE Transactions on Power Systems, Vol: 33, Pages: 3800-3811, ISSN: 0885-8950
Previous work on the role of the demand side in imperfect electricity markets has demonstrated that its self-price elasticity reduces electricity producers' ability to exercise market power. However, the concept of self-price elasticity cannot accurately capture consumers' flexibility, as the latter mainly involves shifting of loads' operation in time. This paper provides for the first time theoretical and quantitative analysis of the beneficial impact of demand shifting (DS) in mitigating market power by the generation side. Quantitative analysis is supported by a multiperiod equilibrium programming model of the imperfect electricity market, accounting for the time-coupling operational constraints of DS as well as network constraints. The decision making process of each strategic producer is modeled through a bi-level optimization problem, which is solved after converting it to a Mathematical Program with Equilibrium Constraints (MPEC) and linearizing the latter through suitable techniques. The oligopolistic market equilibria resulting from the interaction of multiple independent producers are determined by employing an iterative diagonalization method. Case studies on a test market reflecting the general generation and demand characteristics of the GB system quantitatively demonstrate the benefits of DS in mitigating producers' market power, by employing relevant indexes from the literature.
Qiu D, Papadaskalopoulos D, Ye Y, et al., 2018, Investigating the impact of demand flexibility on electricity retailers, PSCC 2018, 20th Power Systems Computation Conference
Fatouros P, Konstantelos I, Papadaskalopoulos D, et al., 2017, A stochastic dual dynamic programming approach for optimal operation of DER aggregators, IEEE PowerTech 2017, Publisher: IEEE
The operation of aggregators of distributed energy resources (DER) is a highly complex task that is affected by numerous factors of uncertainty such as renewables injections, load levels and market conditions. However, traditional stochastic programming approaches neglect information around temporal dependency of the uncertain variables due to computational tractability limitations. This paper proposes a novel stochastic dual dynamic programming (SDDP) approach for the optimal operation of a DER aggregator. The traditional SDDP framework is extended to capture temporal dependency of the uncertain wind power output, through the integration of an n-order autoregressive (AR) model. This method is demonstrated to achieve a better trade-off between solution efficiency and computational time requirements compared to traditional stochastic programming approaches based on the use of scenario trees.
Ye Y, Papadaskalopoulos, Moreira, et al., 2017, Strategic Capacity Withholding by Energy Storage in Electricity Markets, 12th IEEE PES PowerTech Conference, Publisher: IEEE
Abstract:Although previous work has demonstrated the ability of large energy storage (ES) units to exercise market power by withholding their capacity, it has adopted modeling approaches exhibiting certain limitations and has not analyzed the dependency of the extent of exercised market power on ES operating properties. In this paper, the decision making process of strategic ES is modeled through a bi-level optimization problem; the upper level determines the optimal extent of capacity withholding at different time periods, maximizing the ES profit, while the lower level represents endogenously the market clearing process. This problem is solved after converting it to a Mathematical Program with Equilibrium Constraints (MPEC) and linearizing the latter through suitable techniques. Case studies on a test market quantitatively analyze the extent of capacity withholding and its impact on ES profit and social welfare for different scenarios regarding the power and energy capacity of ES.
Moreira R, Ollagnier L, Papadaskalopoulos D, et al., 2017, Optimal Multi-Service Business Models for Electric Vehicles, PowerTech 2017
Papadaskalopoulos, Ye Y, strbac, 2017, Exploring the Role of Demand Shifting in Oligopolistic Electricity Markets, 2017 IEEE Power & Energy Society General Meeting (GM), Publisher: IEEE
Previous work has demonstrated that the priceelasticity of the demand side reduces electricity producers’ability to exercise market power. However, price elasticitycannot capture alone consumers’ flexibility, as the latter mainlyinvolves shifting of loads’ operation in time. This paper providesfor the first time qualitative and quantitative analysis of thevalue of demand shifting in mitigating market power by thegeneration side. An equilibrium programming model of theoligopolistic market setting is developed, taking into account theinter-temporal characteristics of demand shifting. The decisionmaking process of each strategic producer is modelled through abi-level optimization problem, which is solved aftertransforming it to a Mathematical Program with EquilibriumConstraints (MPEC). The market equilibria resulting from theinteraction of multiple independent producers are determinedby employing an iterative diagonalization method. Case studieson a test market with day-ahead horizon and hourly resolutionquantitatively demonstrate the benefits of demand shifting inlimiting generation market power, by employing relevantindexes from the literature.
Papadaskalopoulos D, Strbac G, 2016, Smart price-based scheduling of flexible residential appliances, Smarter Energy: from Smart Metering to the Smart Grid
Ye Y, Papadaskalopoulos D, Strbac G, 2016, An MPEC approach for analysing the impact of energy storage in imperfect electricity markets, 13th International Conference on the European Energy Market (EEM), Publisher: IEEE, ISSN: 2165-4093
Although recent studies have investigated the impacts of energy storage on various aspects of power system operation and planning, its role in imperfect electricity markets has not been explored yet. This paper provides for the first time theoretical and quantitative evidence of the beneficial impact of energy storage in limiting market power by generation companies. Quantitative analysis is supported by a bi-level optimization model of the imperfect electricity market setting, accounting for the time-coupling operational constraints of energy storage. This bi-level problem is solved after converting it to a Mathematical Program with Equilibrium Constraints (MPEC). Case studies are carried out on a test market with day-ahead horizon and hourly resolution.
Ramirez PJ, Papadaskalopoulos D, Strbac G, 2015, Co-Optimization of Generation Expansion Planning and Electric Vehicles Flexibility, IEEE Transactions on Smart Grid, Vol: 7, Pages: 1609-1619, ISSN: 1949-3061
The envisaged de-carbonization of power systems poses unprecedented challenges enhancing the potential of flexible demand. However, the incorporation of the latter in system planning has yet to be comprehensively investigated. This paper proposes a novel planning model that allows co-optimizing the investment and operating costs of conventional generation assets and demand flexibility, in the form of smart-charging/discharging electric vehicles (EV). The model includes a detailed representation of EV operational constraints along with the generation technical characteristics, and accounts for the costs required to enable demand flexibility. Computational tractability is achieved through clustering generation units and EV, which allows massively reducing the number of decision variables and constraints, and avoiding non-linearities. Case studies in the context of the U.K. demonstrate the economic value of EV flexibility in reducing peak demand levels and absorbing wind generation variability, and the dependence of this value on the required enabling cost and users' traveling patterns.
Strbac G, Konstantinidis CV, Moreno R, et al., 2015, It's All About Grids, IEEE POWER & ENERGY MAGAZINE, Vol: 13, Pages: 61-75, ISSN: 1540-7977
Strbac G, Vasilakos Konstantinidis C, Moreno Vieyra R, et al., 2015, It’s All About Grids: The Importance of Transmission Pricing and Investment Coordination in Integrating Renewables, IEEE Power and Energy Magazine, ISSN: 1540-7977
Papadaskalopoulos D, Strbac G, 2015, Nonlinear and Randomized Pricing for Distributed Management of Flexible Loads, IEEE Transactions on Smart Grid, Vol: 7, Pages: 1137-1146, ISSN: 1949-3061
Price-based management of distributed energy resources within microgrids is continuously gaining ground due to scalability and privacy limitations of centralized architectures. However, the concentration of flexible loads' response to the lowest-priced periods yields inefficient solutions. A previously proposed measure imposing a flexibility restriction on flexible loads might raise acceptability and feasibility concerns by the users. This paper develops a novel fully price-based approach where this hard restriction is replaced by a soft nonlinear price signal. This signal is customized to the operating properties of the different flexible load types by penalizing the square of the demand and the duration of cycle delay of loads with continuously adjustable power levels and deferrable cycles, respectively. This approach is shown to produce more efficient solutions than the flexibility restriction measure for both types of loads. For the latter type, randomization of the nonlinear prices brings additional benefits, especially in low operating diversity cases. These contributions are supported by case studies on a microgrid test system with electric vehicles and wet appliances used as representative examples of the above flexible load types.
Strbac G, Hatziargyriou N, Lopes JP, et al., 2015, Microgrids, IEEE Power & Energy Magazine, Vol: 13, Pages: 35-43, ISSN: 1540-7977
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- Citations: 103
Strbac G, Aunedi M, Papadaskalopoulos D, et al., 2015, Modelling Requirements for Least-Cost and Market-Driven Whole-System Analysis
Papadaskalopoulos D, Pudjianto D, Strbac G, 2014, Decentralized Coordination of Microgrids With Flexible Demand and Energy Storage, IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, Vol: 5, Pages: 1406-1414, ISSN: 1949-3029
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- Citations: 55
Ye Y, Papadaskalopoulos D, Strbac G, 2014, Factoring Flexible Demand Non-Convexities in Electricity Markets, IEEE Transactions on Power Systems, Vol: 30, Pages: 2090-2099, ISSN: 0885-8950
Ye Y, Papadaskalopoulos D, Strbac G, 2014, Pricing Flexible Demand Non-Convexities in Electricity Markets, 18th Power Systems Computation Conference (PSCC), Publisher: IEEE, Pages: 1-7, ISSN: 1944-9933
Uniform marginal prices cannot generally support competitive equilibrium solutions in markets with non-convexities and yield schedules' inconsistency and surplus sub-optimality effects. Previous work has identified non-convexities associated with the generation side of electricity markets and proposed a generalized uplift approach to eliminate these effects. This paper examines the above issues from the perspective of the flexible demand (FD) side. FD non-convexities are identified, including its ability to forgo demand activities and minimum power levels, and resulting inconsistency and surplus sub-optimality effects are demonstrated through simple examples. Generalized uplift functions for FD participants are proposed, including quadratic pricing terms to limit their tendency to concentrate at the lowest-priced periods, and binary terms associated with their ability to forgo activities. Finally, a new rule is proposed for the equitable distribution of the total surplus loss among the market participants. These contributions are supported by case studies on a market with a day-ahead horizon and hourly resolution.
Papadaskalopoulos D, Strbac G, 2013, Decentralized Participation of Flexible Demand in Electricity Markets-Part I: Market Mechanism, IEEE TRANSACTIONS ON POWER SYSTEMS, Vol: 28, Pages: 3658-3666, ISSN: 0885-8950
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- Citations: 119
Papadaskalopoulos D, Strbac G, Mancarella P, et al., 2013, Decentralized Participation of Flexible Demand in Electricity Markets-Part II: Application With Electric Vehicles and Heat Pump Systems, IEEE TRANSACTIONS ON POWER SYSTEMS, Vol: 28, Pages: 3667-3674, ISSN: 0885-8950
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- Citations: 109
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