Publications
76 results found
Ye Y, Wang H, Tang Y, et al., 2022, Real-time Autonomous Optimal Energy Management Strategy for Residents Based on Deep Reinforcement Learning, Dianli Xitong Zidonghua/Automation of Electric Power Systems, Vol: 46, Pages: 110-119, ISSN: 1000-1026
Alongside the wide proliferation of distributed energy resources at the residential sector, how to meet the needs of real-time autonomous energy management while considering the heterogeneous operating characteristics of these resources so as to maximize the utility for residential end-users deserves significant research attention. In this area, conventional model-based optimization methods are generally burdened with inaccurate system modeling and inability to efficiently deal with uncertainties stemmed from multiple sources. In order to address these challenges, this paper proposes a model-free method based on deep reinforcement learning to achieve real-time autonomous energy management optimization. First, the user's resources are classified into different categories, their operating characteristics are then described using a unified 3-element tuple, and the associated energy management actions are also identified. Next, the long short-term memory neural network is employed to extract the future trends of multi-source sequential data from the environment states. Then, based on the proximal policy optimization algorithm,it enables efficient learning of the optimal energy management policies in the multi-dimensional continuous-discrete mixed action space, which can adaptively adjust to system uncertainties towards the user's electricity cost minimization objective. Finally, the effectiveness of the proposed method is verified by benchmarking its performance against several existing methods through case studies on an actual scenario.
Sun W, Wang Q, Ye Y, et al., 2022, Unified modelling of gas and thermal inertia for integrated energy system and its application to multitype reserve procurement, APPLIED ENERGY, Vol: 305, ISSN: 0306-2619
- Author Web Link
- Cite
- Citations: 5
Ye Y, Tang Y, Wang H, et al., 2021, A Scalable Privacy-Preserving Multi-agent Deep Reinforcement Learning Approach for Large-Scale Peer-to-Peer Transactive Energy Trading, IEEE Transactions on Smart Grid, Pages: 1-1, ISSN: 1949-3053
Peer-to-peer (P2P) transactive energy trading has emerged as a promising paradigm towards maximizing the flexibility value of prosumers’ distributed energy resources (DERs). Despite reinforcement learning constitutes a well-suited model-free and data-driven methodological framework to optimize prosumers’ energy management decisions, its application to the large-scale coordinated management and P2P trading among multiple prosumers within an energy community is still challenging, due to the scalability, non-stationarity and privacy limitations of state-of-the-art multi-agent deep reinforcement learning (MADRL) approaches. This paper proposes a novel P2P transactive trading scheme based on the multi-actor-attention-critic (MAAC) algorithm, which addresses the above challenges individually. This method is complemented by a P2P trading platform that incentivizes prosumers to engage in local energy trading while also penalizes each prosumer’s addition to rebound peaks. %The proposed method is applied to the coordination of prosumers operating multiple and diverse DERs, including photovoltaic (PV) generators, energy storage (ES) units and two types of shiftable loads. Case studies involving a real-world, large-scale scenario with 300 residential prosumers demonstrate that the proposed method significantly outperforms the state-of-the-art MADRL methods in reducing the community’s cost and peak demand.
Li J, Ye Y, Papadaskalopoulos D, et al., 2021, Distributed consensus-based coordination of flexible demand and energy storage resources, IEEE Transactions on Power Systems, Vol: 36, Pages: 3053-3069, ISSN: 0885-8950
Distributed, consensus-based algorithms have emerged as a promising approach for the coordination of distributed energy resources (DER) due to their communication, computation, privacy and reliability advantages over centralized approaches. However, state-of-the-art consensus-based algorithms address the DER coordination problem in independent time periods and therefore are inherently unable to capture the time-coupling operating characteristics of flexible demand (FD) and energy storage (ES) resources. This paper demonstrates that state-of-the-art algorithms fail to converge when these time-coupling characteristics are considered. In order to address this fundamental limitation, a novel consensus-based algorithm is proposed which includes additional consensus variables. These variables express relative maximum power limits imposed on the FD and ES resources which effectively mitigate the concentration of the FD and ES responses at the same time periods and steer the consensual outcome to a feasible and optimal solution. The convergence and optimality of the proposed algorithm are theoretically proven while case studies numerically demonstrate its convergence, optimality, robustness to initialization and information loss, and plug-and-play adaptability.
Qiu D, Ye Y, Papadaskalopoulos D, et al., 2021, Scalable coordinated management of peer-to-peer energy trading: A multi-cluster deep reinforcement learning approach, APPLIED ENERGY, Vol: 292, ISSN: 0306-2619
- Author Web Link
- Cite
- Citations: 47
Ye Y, Qiu D, Wang H, et al., 2021, Real-Time Autonomous Residential Demand Response Management Based on Twin Delayed Deep Deterministic Policy Gradient Learning, ENERGIES, Vol: 14
- Author Web Link
- Cite
- Citations: 19
Li J, Ye Y, Papadaskalopoulos D, et al., 2021, Computationally Efficient Pricing and Benefit Distribution Mechanisms for Incentivizing Stable Peer-to-Peer Energy Trading, IEEE Internet of Things Journal, Vol: 8, Pages: 734-749
Peer-to-peer (P2P) energy trading has emerged as a promising market paradigm towards maximizing the value of distributed energy resources (DER) for electricity prosumers, by enabling direct energy trading among them. However, state-of-the-art P2P mechanisms either fail to adequately incentivize prosumers to participate, prevent prosumers from accessing the highest achievable monetary benefits, or suffer severely from the curse of dimensionality. This paper proposes two computationally efficient mechanisms to construct a stable grand coalition of prosumers participating in P2P trading, founded on cooperative game-theoretic principles. The first one involves a benefit distribution scheme inspired by the core tâtonnement process while the second involves a novel pricing mechanism based on the solution of a single linear program. The performance of the proposed mechanisms is validated against state-of-the-art mechanisms through numerous case studies using real-world data. The results demonstrate that the proposed mechanisms exhibit superior computational performance than the nucleolus and are superior to the rest of the examined mechanisms in incentivizing prosumers to remain in the grand coalition.
Chen P, Ye Y, Hu J, et al., 2021, Dynamic Modeling of Smart Buildings Energy Consumption: A Cyber-Physical Fusion Approach, Pages: 2243-2247
Energy consumption modeling constitutes an imperative step towards improving energy efficiency of smart buildings. In this context, conventional static modeling methods are not fully accurate, since the energy consumption is generally affected by multiple time-dependent factors. In order to address this limitation, this paper proposes a dynamic modeling method based on cyber-physical fusion techniques. First, the physical model of energy consumption simulation involving multiple time windows is constructed. Second, combined with the analysis of key processes and real scenarios, the heat exchange coefficient of the building envelope and the correction coefficient of air-conditioning cooling capacity are identified as key parameters for the consumption model. Subsequently, a novel parameter identification method based on one-dimensional deep convolutional network is proposed to determine the above key parameters, which are later employed by the physical model to further calibrate its accuracy. Case studies on a real-world building environment demonstrate that the proposed cyber-physical method outperforms the conventional physical methods in terms of better accuracy for energy consumption estimation.
Wang H, Ye Y, Tang Y, 2021, Towards Market-Based Integration of Renewable Generation in Power Grids, Pages: 557-562
The integration of renewable generation in power grids has been conventionally examined from a techno-economic perspective, aiming at identifying the upper bounds of their integrated capacity without exceeding the allowable system operational performance boundaries. In light of the electrical industry's liberalization and the anticipated implementation of more cost-reflective and dynamic energy prices at all voltage levels, this paper sheds new light on the optimization of renewable hosting capacity from a market viewpoint. It addresses an important challenge associated with the plummeting of electricity prices in face of increased penetration of renewables, which threatens the recovery of investment costs and restrains profitable integration of renewables to the grid. To this end, we propose a novel bilevel optimization model which employs an upper level problem attempting to maximize the renewable generation capacity without violating its long-term profitability constraint, and a lower level problem where the market clearing process is shaped. Case studies on the basis of a 2-node system corroborate the significant value of the proposed model and quantitatively analyze the dependence of the renewable generation investments on the grid's network capacity.
Yuan Q, Ye Y, Tang Y, et al., 2021, Optimal Load Scheduling in Coupled Power and Transportation Networks, IEEE IAS Conference on Industrial and Commercial Power System Asia (IEEE I and CPS Asia), Publisher: IEEE, Pages: 1512-1517
- Author Web Link
- Cite
- Citations: 1
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.
Li J, Ye Y, Strbac G, 2020, Stabilizing peer-to-peer energy trading in prosumer coalition through computational efficient pricing, Electric Power Systems Research, Vol: 189
Load balancing issues in distribution networks have emerged alongside the large-scale deployment of distributed renewable generation sources. In light of this challenge, peer-to-peer (P2P) energy trading constitutes a promising approach for delivering secure and economic supply-demand balance when faced with variable load and intermittent renewable generation through matching energy demand and supply locally. However, state-of-the-art mechanisms for governing P2P energy trading either fail to suitably incentivize prosumers to participate in P2P trading or suffer severely from the curse of dimensionality with their computational complexity increase exponentially with the number of prosumers. In this paper, a P2P energy trading mechanism based on cooperative game theory is proposed to establish a grand energy coalition of prosumers and a computationally efficient pricing algorithm is developed to suitably incentivize prosumers for their sustainable participation in the grand coalition. The performance of the proposed algorithm is demonstrated by comparing it to state-of-the-art mechanisms through numerous case studies in a real-world scenario. The superior computational performance of the proposed algorithm is also validated.
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.
Li J, Ye Y, Strbac G, 2020, Stabilizing Peer-to-Peer Energy Trading in Prosumer Coalition Through Computational Efficient Pricing, 21st Power Systems Computation Conference
Load balancing issues in distribution networks have emerged alongside the large-scale deployment of distributed renewable generation sources. In light of this challenge, peer-to-peer (P2P) energy trading constitutes a promising approach for delivering secure and economic supply-demand balance when faced with variable load and intermittent renewable generation through matching energy demand and supply locally. However, state-of-the-art mechanisms for governing P2P energy trading either fail to suitably incentivize prosumers to participate in P2P trading or suffer severely from the curse of dimensionality with their computational complexity increase exponentially with the number of prosumers. In this paper, a P2P energy trading mechanism based on cooperative game theory is proposed to establish a grand energy coalition of prosumers and a computationally efficient pricing algorithm is developed to suitably incentivize prosumers for their sustainable participation in the grand coalition. The performance of the proposed algorithm is demonstrated by comparing it to state-of-the-art mechanisms through numerous case studies in a real-world scenario. The superior computational performance of the proposed algorithm is also validated.
Ye Y, Qiu D, Ward J, et al., 2020, Model-Free Real-Time Autonomous Energy Management for a Residential Multi-Carrier Energy System: A Deep Reinforcement Learning Approach, 29th International Joint Conference on Artificial Intelligence - 17th Pacific Rim International Conference on Artificial Intelligence (IJCAI-PRICAI2020)
Qiu D, Ye Y, Papadaskalopoulos D, et al., 2020, 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, 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, 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, Pages: 1-1, 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.
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, Vol: 11, Pages: 3068-3068, 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, 2020, 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, Vol: 14, Pages: 1739-1750, 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.
Valenzuela E, Moreno R, Papadaskalopoulos D, et al., 2020, Exploring the concept of hosting capacity from an electricity market perspective, Hosting Capacity for Smart Power Grids, Pages: 223-247, ISBN: 9783030400286
Previous work has introduced and analyzed the concept of hosting capacity of electricity grids from a technical perspective, determining the upper bounds for integrating various types of renewable energy resources without jeopardizing reliability and quality of supply. In order to achieve that, various hosting capacity metrics have been proposed to calculate such upper bounds, mainly focusing on the technical balancing and network challenges associated with a large-scale integration of renewable generation in electricity networks. In view of the deregulation of the electricity industry and the expectation to apply more cost-reflective and dynamic energy prices with increased spatiotemporal granularity at all voltage levels, this chapter investigates, for the first time, the concept of hosting capacity from a market perspective, introducing the concept of market hosting capacity (MHC). This new concept tackles the market-related challenge associated with the corresponding decrease in energy prices while renewables’ penetration levels increase, which naturally threatens renewables’ investment cost recovery and hence limits the capacity of renewable generation that can be profitably integrated to the grid. In this context, a bi-level optimization model is developed, where the upper level maximizes the renewable generation capacity subject to its long-term profitability constraint and the lower level represents the market clearing process. This bi-level problem is solved after converting it to a single-level mixed-integer linear problem (MILP). Two theoretical case studies on a 2-node and a 24-node test system along with a practical case study in the Chilean transmission system demonstrate that the MHC is enhanced with increasing renewable generation subsidies and increasing flexibility of the conventional generation mix, while its dependence on the network capacity does not follow a uniform trend. Finally, we provide relevant discussions on how this new con
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, Sun M, et al., 2020, Deep Reinforcement Learning for Strategic Bidding in Electricity Markets, IEEE-Power-and-Energy-Society General Meeting (PESGM), Publisher: IEEE, ISSN: 1944-9925
Ye Y, Papadaskalopoulos D, Kazempour J, et al., 2020, Incorporating Non-Convex Operating Characteristics into Bi-Level Optimization Electricity Market Models, IEEE-Power-and-Energy-Society General Meeting (PESGM), Publisher: IEEE, ISSN: 1944-9925
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.
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.
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, Li J, et al., 2019, Multi-period and Multi-spatial Equilibrium Analysis in Imperfect Electricity Markets: A Novel Multi-Agent Deep Reinforcement Learning Approach, IEEE Access, ISSN: 2169-3536
Oderinwale T, Ye Y, Papadaskalopoulos D, et al., 2019, 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.
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.