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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. [ABSTRACT FROM AUTHOR]

The increasing penetration of small-scale distributed energy resources (DER) has the potential to support cost-efficient energy balancing in emerging electricity systems, but is also fundamentally affecting the conventional operation paradigm of the latter. In this context, innovative market mechanisms need to be devised to better coordinate and provide incentives for DER to utilize their flexibility. Peer-to-Peer (P2P) energy trading has emerged as an alternative approach to facilitate direct trading between consumers and prosumers interacting in an energy collective and fosters more efficient local demand–supply balancing. While previous research has primarily focused on the technical and economic benefits of P2P trading, little effort has been made towards the incorporation of prosumers' heterogeneous characteristics in the P2P trading problem. Here, we address this research gap by classifying the participating prosumers into multiple clusters with regard to their portfolio of DER, and analyzing their trading decisions in a simulated P2P trading platform. The latter employs the mid-market rate (MMR) local pricing mechanism to enable energy trading among prosumers and penalizes the contribution to the system demand peak of each prosumer. We formulate the P2P trading problem as a multi-agent coordination problem and propose a novel multi-agent deep reinforcement learning (MADRL) method to address it. The proposed method is founded on the combination of the multi-agent deep deterministic policy gradient (MADDPG) algorithm and the technique of parameter sharing (PS), which not only enables accelerating the training speed by sharing experiences and learned policies between all agents in each cluster, but also sustains the policies' diversity between multiple clusters. To address the non-stationarity and computational complexity of MADRL as well as persevering the privacy of prosumers, the P2P trading platform acts as a trusted third party which augments the market collective trading information to help training of prosumer agents. Experiments with a large-scale real-world data-set involving 300 residential households demonstrate that the proposed MADRL method exhibits a strong generalization capability in the test data-set and outperforms the state-of-the-art MADRL methods with regard to the system operation cost, demand peak as well as computational time. • The coordinated management of large-scale P2P energy trading is investigated. • The heterogeneity of prosumers with diverse resources' portfolios is accounted. • A novel multi-agent deep reinforcement learning approach is proposed. • The proposed approach achieves significant operating cost and peak demand benefits. • The proposed approach deals effectively with uncertain parameters of the problem. [ABSTRACT FROM AUTHOR]

• P2P energy trading is modeled as a prosumer coalitional game • A computationally efficient pricing mechanism is proposed to construct a stable grand coalition of prosumers • The effectiveness of the proposed mechanism is validated against state-of-the-art mechanisms using real-world system data 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. [ABSTRACT FROM AUTHOR]

• Local energy markets enable direct energy trading among participating customers. • The interactions among electricity retailers and local energy markets are modelled. • A novel approach is employed for solving the developed bi-level optimization model. • Local energy markets are shown to enhance the competitiveness of retailers. • Local energy markets are shown to increase the economic surplus of the customers. 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. [ABSTRACT FROM AUTHOR]

The increasing effects of global warming and energy depletion have raised concerns about the pollution caused by traditional oil and fossil energy usage. Distributed energy resources (DERs) have emerged as promising techniques to address these issues. However, the growing proportion of power injection from DERs presents technical challenges such as power quality decline and power flow reversal, which may destabilize the grid. Non-intrusive load monitoring (NILM) is a promising and cost-effective approach that can provide residential power information to improve grid scheduling, dispatching, and optimize residential power consumption behavior. This paper proposes a novel NILM method that can decompose the power of both household appliances and DERs integrated with residential houses. The proposed approach employs a data segment method that utilizes adaptive window lengths to identify the behavior of DERs based on the operating periods and characteristics of different equipment. A non-intrusive load monitoring method is proposed based on the ResNet-seq2seq framework to address the problem of model degradation and gradient vanishing. Post-processing techniques are applied to the output results using a feature list of the target equipment. Experiments are conducted on a dataset organized based on the REDD and Pecan Street datasets to demonstrate the accuracy and effectiveness of the proposed method. The results show that compared to the start-of-the-art CNN-seq2seq model, the proposed method achieved 54.95%, 69.18%, 56.95%, and 84.98% reduction in MAE for PV, EV, refrigerators, and microwaves, respectively. • A novel ResNet-seq2seq-based NILM approach is proposed for load disaggregation of residential houses integrating DERs. • Wavelet denoising and adaptive window length data segments are used for data pre-processing and accurate disaggregation. • Residual connections are employed to solve model degradation and vanishing gradient issues as network depth increases. • A post-processing technique is applied to modify the output of the network and improve disaggregation accuracy. [ABSTRACT FROM AUTHOR]

The increasing frequency of the occurrence of high impact low probability (HILP) disruptive events has posed huge threats to the power system. Therefore, power system resilience improvement has drawn world-wide attention. Electric buses (EB) are equipped with a large capacity of batteries which could provide sufficient energy and capacity values to run islanded micro-grids (MG) for hours, with a particular focus on V2G, which will deliver system resilience during extreme operational conditions. In this paper, an innovative reciprocal transport-power system coordination scheme is proposed to simultaneously mitigate the adverse impacts on both systems. By using this economic incentive-free approach, EBs are endogenously motivated to provide resilience-oriented V2G service to the power system, while increasing the transport service survivability during HILP disruptive events. Additionally, a novel EB dispatch rescheduling method considering the trade-off of both the requirement of energy supply security and transport service fulfilment is proposed. By using this method, the essential load curtailment of local MGs can be further alleviated, while the impacts of reduced charging service on transport service can also be further mitigated. Through a series of case studies, we illustrate that EBs have significant potential to provide resilience enhancement to the urban energy system based on appropriate dispatch strategies. Meanwhile, the interests of the power system and the transport system can be well reconciled by using the proposed approach during HILP disruptive events. • Present an algorithm for resilient operation of power system and transport system. • Propose a reciprocal coordination scheme reconciling the interests of both systems. • Investigate the values of strategic EB dispatch rescheduling in improving resilience. • Quantify energy values and capacity values of EBs in enhancing system resilience. [ABSTRACT FROM AUTHOR]

• Proposed an original concept called spatial load migration (SLM), which is supposed to fully utilize the space connection between power loads, and provide a new load dispatch approach to address power transmission limitation. • Analyzed the feasibility and constraints of the typical power loads applied on SLM base on a holistic literature review and a utilized assessment architecture, including data center, base station, electric vehicle, mobile storage, and multi-energy equipment. • Provided a brief case study describing the potential of SLM on effective power distribution. • Concluded the power level and accessible appliance of each load type, and discussed the future research direction of SLM. As the transmission capabilities of power systems face challenges from renewable generation and massive electricity usage, power load resources have an increasingly important role in power dispatch. Traditional load dispatch methods are mainly based on the dispatch of load behavior in the time dimension, disregarding the potential of heterogeneous space connections existing in loads, such as communication connections and transportation connections. Although some studies, such as energy hubs or data networks, have utilized some part of space connections, no unified framework has been proposed to distinguish the problems caused by applying space connections or a horizontal review of spatial resources. In this paper, a framework named spatial load migration (SLM), which provides solutions to solving spatial network constraint problems with the enhancement of space connections, is proposed. With respect to SLM, this paper analyzes the mechanisms of typical power load types participating in SLM and qualitative appliance potential. A multi-system-coupled case study is designed with the occasion of power line disconnection. The results show that applying space connections to load dispatch can both expand the controllable objects and increase system performance. [ABSTRACT FROM AUTHOR]