28 results found
Oderinwale T, Papadaskalopoulos D, Ye Y, et al., 2020, Investigating the impact of flexible demand on market-based generation investment planning, International Journal of Electrical Power & Energy Systems, Vol: 119, Pages: 105881-105881, ISSN: 0142-0615
Demand flexibility has attracted significant interest given its potential to address techno-economic challenges associated with the decarbonisation of electricity systems. However, previous work has investigated its long-term impacts through centralized generation planning models which do not reflect the current deregulated environment. At the same time, existing market-based generation planning models are inherently unable to capture the demand flexibility potential since they neglect time-coupling effects and system reserve requirements in their representation of the electricity market. This paper investigates the long-term impacts of demand flexibility in the deregulated environment, by proposing a time-coupling, bi-level optimization model of a self-interested generation company’s investment planning problem, which captures for the first time the energy shifting flexibility of the demand side and the operation of reserve markets with demand side participation. Case studies investigate different cases regarding the flexibility of the demand side and different market design options regarding the allocation of reserve payments. The obtained results demonstrate that, in contrast with previous centralised planning models, the proposed model can capture the dependency of generation investment decisions and the related impacts of demand flexibility on the electricity market design and the subsequent strategic response of the self-interested generation company.
Qiu D, Ye Y, Papadaskalopoulos D, et al., A Deep Reinforcement Learning Method for Pricing Electric Vehicles with Discrete Charging Levels, IEEE Transactions on Industry Applications, ISSN: 0093-9994
The effective pricing of electric vehicles (EV) charging by aggregators constitutes a key problem towards the realization of the significant EV flexibility potential in deregulated electricity systems, and has been addressed by previous work through bi-level optimization formulations. However, the solution approach adopted in previous work cannot capture the discrete nature of the EV charging / discharging levels. Although reinforcement learning (RL) can tackle this challenge, state-of-the-art RL methods require discretization of state and / or action spaces and thus exhibit limitations in terms of solution optimality and computational requirements. This paper proposes a novel deep reinforcement learning (DRL) method to solve the examined EV pricing problem, combining deep deterministic policy gradient (DDPG) principles with a prioritized experience replay (PER) strategy, and setting up the problem in multi-dimensional continuous state and action spaces. Case studies demonstrate that the proposed method outperforms state-of-the-art RL methods in terms of both solution optimality and computational requirements, and comprehensively analyze the economic impacts of smart-charging and vehicle-to-grid (V2G) flexibility on both aggregators and EV owners.
Ye Y, Qiu D, Wu X, et al., 2020, Model-Free Real-Time Autonomous Control for A Residential Multi-Energy System Using Deep Reinforcement Learning, IEEE Transactions on Smart Grid, ISSN: 1949-3053
Multi-energy systems (MES) are attracting increasing attention driven by its potential to offer significant flexibility in future smart grids. At the residential level, the roll-out of smart meters and rapid deployment of smart energy devices call for autonomous multi-energy management systems which can exploit real-time information to optimally schedule the usage of different devices with the aim of minimizing end-users’ energy costs. This paper proposes a novel real-time autonomous energy management strategy for a residential MES using a model-free deep reinforcement learning (DRL) based approach, combining state-of-the-art deep deterministic policy gradient (DDPG) method with an innovative prioritized experience replay strategy. This approach is tailored to align with the nature of the problem by posing it in multi-dimensional continuous state and action spaces, facilitating more cost-effective control strategies to be devised. The superior performance of the proposed approach in reducing end-user’s energy cost while coping with the MES uncertainties is demonstrated by comparing it against state-of-the-art DRL methods as well as conventional stochastic programming and robust optimization methods in numerous case studies in a real-world scenario.
Li J, Ye Y, Strbac G, Incentivizing Peer-to-Peer Energy Sharing Using a Core Tâtonnement Algorithm, 2020 IEEE IEEE Power & Energy Society General Meeting
Qiu D, Papadaskalopoulos D, Ye Y, et al., 2020, Investigating the Effects of Demand Flexibility on Electricity Retailers’ Business through a Tri-Level Optimization Model, IET Generation, Transmission & Distribution, ISSN: 1751-8687
The investigation of the effects of demand flexibility on the pricing strategies and the profits of electricity retailers has recently emerged as a highly interesting research area. However, the state-of-the-art, bi-level optimization modelling approach makes the unrealistic assumption that retailers treat wholesale market prices as exogenous, fixed parameters. This paper proposes a tri-level optimization model which drops this assumption and represents endogenously the wholesale market clearing process, thus capturing the realistic implications of a retailer’s pricing strategies and the resulting demand response on the wholesale market prices. The scope of the examined case studies is threefold. First of all, they demonstrate the interactions between the retailer, the flexible consumers and the wholesale market and analyse the fundamental effects of the consumers’ time-shifting flexibility on the retailer’s revenue from the consumers, its cost in the wholesale market, and its overall profit. Furthermore, they analyse how these effects of demand flexibility depend on the retailer’s relative size in the market and the strictness of the regulatory framework. Finally, they highlight the added value of the proposed tri-level model by comparing its outcomes against the state-of-the-art bi-level modelling approach.
Sun M, Wang Y, Teng F, et al., 2019, Clustering-based residential baseline estimation: a probabilistic perspective, IEEE Transactions on Smart Grid, Vol: 10, Pages: 6014-6028, ISSN: 1949-3061
Demand Response (DR) is one of the most cost-effective solutions for providing flexibility to power systems. The extensive deployment of DR trials and the roll-out of smart meters enable the quantification of consumer responsiveness to price signals via baseline estimation. The traditional deterministic baseline estimation approach can provide only a single value without consideration of uncertainty. This paper proposes a novel probabilistic baseline estimation framework that consists of a daily load profile pool construction stage, a deep learning-based clustering stage, an optimal cluster selection stage, and a quantile regression forests model construction stage. In particular, the concept of a daily load profile pool is introduced, and a deep-learning-based clustering approach is employed to handle a large number of daily patterns to further improve the baseline estimation performance. Case studies have been conducted on fine-grained smart meter data collected from a real dynamic time-of-use (dTOU) tariffs trial of the Low Carbon London (LCL) project. The superior performance of the proposed method is demonstrated based on a series of evaluation metrics regarding both deterministic and probabilistic estimation results.
Li J, Ye Y, Papadaskalopoulos D, et al., 2019, Consensus-Based Coordination of Time-Shiftable Flexible Demand, 2019 International Conference on Smart Energy Systems and Technologies (SEST), Publisher: IEEE
Distributed, consensus-based algorithms constitute a promising approach for the coordination of distributed energy resources (DER) due to their practical advantages over centralized approaches. However, state-of-the-art consensus-based algorithms address the coordination problem in independent time periods and therefore are inherently unable to capture the time-shifting flexibility of the demand side. This paper demonstrates that state-of-the-art algorithms fail to converge when time-shiftable flexible demands (TSFD) are present. In order to address this fundamental limitation, a relative maximum power restriction is introduced, which effectively mitigates the concentration of the TSFD responses at the same time periods and steers the consensus-based algorithm towards a feasible and near-optimal solution.
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, Qiu D, Li J, et al., Multi-period and Multi-spatial Equilibrium Analysis in Imperfect Electricity Markets: A Novel Multi-Agent Deep Reinforcement Learning Approach, IEEE Access, ISSN: 2169-3536
Ye Y, Qiu D, Sun M, et al., 2019, Deep Reinforcement Learning for Strategic Bidding in Electricity Markets, IEEE TRANSACTIONS ON SMART GRID, Vol: 11, Pages: 1343-1355, ISSN: 1949-3061
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., 2019, Incorporating Non-Convex Operating Characteristics into Bi-Level Optimization Electricity Market Models, IEEE Transactions on Power Systems, Pages: 1-1, ISSN: 0885-8950
Takis-Defteraios G, Papadaskalopoulos D, Ye Y, et al., Role of Flexible Demand in Supporting Market-Based Integration of Renewable Generation, 13th IEEE PES PowerTech Conference, Publisher: IEEE
Previous work has analyzed the renewable generation hosting capacity of electricity systems and the relevant value of flexible demand from a techno-economic perspective, considering the balancing and network challenges associated with a large-scale integration of renewables. This paper investigates these issues from a market perspective, considering the challenges of low energy prices and renewables’ investment cost recovery. In this context, a multi-period bi-level optimization model is developed, where the upper level maximizes the renewable generation capacity subject to the long-term profitability constraint and the lower level represents the market clearing process, accounting for the time-coupling operational constraints of flexible demand. This bi-level problem is solved after converting it to a single-level mixed-integer linear problem (MILP). Case studies validate the proposed model and demonstrate that demand flexibility increases the maximum renewable generation capacity that can be integrated in the system without violating its profitability constraint.
Papadaskalopoulos D, Ye Y, Oderinwale T, et al., A Bi-Level Optimization Modeling Framework for Investigating the Role of Flexible Demand in Deregulated Electricity Systems, 19th International Conference on Environment and Electrical Engineering (19th IEEE EEEIC)
Flexible demand technologies have attracted significant interest due to their potential value in enhancing the cost-efficiency of low-carbon electricity systems. However, this value has been investigated in the existing literature through traditional centralized models, which are not aligned with the recent deregulation of the electricity industry. This paper presents a bi-level optimization modeling framework which is capable of capturing the strategic behavior of self-interested market players in deregulated electricity systems while accounting for the time-coupling operating characteristics of flexible demand. This modeling framework is applied to investigate two highly interesting issues around the role of flexible demand, namely: a) its impact on the market power exercised by large electricity generators, b) its impact on strategic generation investment planning decisions. Case studies quantitatively demonstrate that flexible demand mitigates the market power of electricity generators in short-term electricity markets and reduces significantly the long-term system costs.
Oderinwale T, Ye Y, Papadaskalopoulos D, et al., Impact of energy storage on market-based generation investment planning, 13th IEEE PES PowerTech Conference Milano 2019, Publisher: IEEE
Previous work has analyzed the role of energy storage (ES) on generation investment planning through centralised cost-minimization models which are inherited from the era of regulated electricity utilities. This paper investigates this issue in the context of the deregulated market environment by proposing a new strategic generation investment planning model. The decision making of a strategic generation company is modeled through a multi-period bi-level optimization problem, where the upper level determines the profit-maximizinginvestment decisions of the generation company and the lower level represents themarket clearing process, accounting for the time-coupling operational characteristics of ES. This bi-level problem is solved after converting it to a single-level mixed-integer linear problem (MILP). Case studies demonstrate thatthe introduction of ES reduces the total generation capacity investment and enhances investments in “must-run” baseload generation over flexible peaking generation, yielding significant system cost savings.
Strbac G, Pudjianto D, Aunedi M, et al., 2019, Cost-effective decarbonization in a decentralized market the benefits of using flexible technologies and resources, IEEE Power and Energy Magazine, Vol: 17, Pages: 25-36, ISSN: 1540-7977
Ye Y, Papadaskalopoulos D, Moreira R, et al., Investigating the Impacts of Price-Taking and Price-Making Energy Storage in Electricity Markets through an Equilibrium Programming Model, IET Generation, Transmission & Distribution
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.
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
Qiu D, Papadaskalopoulos D, Ye Y, et al., Investigating the impact of demand flexibility on electricity retailers, PSCC 2018, 20th Power Systems Computation Conference
Ye Y, Huang W, Ma G, et al., 2018, Cause analysis and policy options for the surplus hydropower in southwest China based on quantification, JOURNAL OF RENEWABLE AND SUSTAINABLE ENERGY, Vol: 10, ISSN: 1941-7012
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.
Ye Y, 2017, Modelling and analysing the integration of flexible demand and energy storage in electricity markets
Papadaskalopoulos, Ye Y, strbac, 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.
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.
Ye Y, Papadaskalopoulos D, Strbac G, 2016, Factoring Flexible Demand Non-Convexities in Electricity Markets, IEEE-Power-and-Energy-Society General Meeting (PESGM), Publisher: IEEE, ISSN: 1944-9925
Ye Y, Papadaskalopoulos D, Strbac G, 2015, 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.
Ye Y, Papadaskalopoulos D, Strbac G, 2014, Pricing flexible demand non-convexities in electricity markets, 2014 Power Systems Computation Conference (PSCC), Publisher: IEEE
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