Transitioning to net-zero emission energy systems is currently on the agenda in various countries to tackle climate change, a global challenge that threatens the lives of future generations. To fully decarbonize energy systems, a radical paradigm shift through deep integration of renewable resources supported by storage technologies is envisaged in multisector energy systems, especially in the electric power sector. As a result, inverter-based resources (IBRs), mainly wind, photovoltaics (PVs), and batteries, will dominate the electric power grids. This transition involves phasing out conventional fossil fuel-based plants and decommissioning associated synchronous machines, the grid’s primary reactive power sources. The ongoing removal of these primary reactive power sources introduces critical operational challenges that could compromise the reliability and stability of the grid. The inverters used for integrating IBRs can deliver diverse crucial ancillary services, particularly reactive power support. However, the potential of IBRs to address reactive power requirements in future decarbonized grids still needs to be fully addressed. The existing literature lacks a comprehensive approach to coordinating and harmonizing the efforts of various stakeholders and drivers to leverage the reactive power capability of IBRs.To bridge this gap, this article thoroughly reviews the reactive power implications for future grids with a considerable share of primary IBRs, comprising distributed and large-scale wind, PV and battery storage plants. This article starts with a summary of the concept, measurement methods, and importance of reactive power for voltage control and how it is managed today utilizing conventional sources. The reactive power transition from current to future grids within the context of the greater energy transition is then discussed by shedding light on its diverse aspects. Afterward, the reactive capability curve of each IBR is derived from the equivalent circuits and equations. Various grid codes and integration requirements of IBRs are then analyzed from a reactive power support viewpoint. Also, the concepts related to reactive power and voltage control comprising control extents, modes, and techniques are elaborated. Finally, recommendations are provided to set the stage for leveraging the capabilities of IBRs to address the reactive power requirements of future grids. The presented material sheds light on the pivotal role of reactive power in future grids and provides a roadmap for policymakers, utilities, and grid operators to manage a seamless transition to a decarbonized grid.

The coordinated operation of grid-interactive buildings contributes to creating a more resilient and reliable power grid. However, existing studies fail to identify the demand changes of each building resulting from their coordinated participation in providing grid services, which affects the economic compensation of each building and its willingness to coordinate. In this article, we investigate an optimal coordinated operation problem for grid-interactive residential buildings (GRBs) while considering generation capacity services and economic compensation for participating GRBs. Specifically, we first formulate two optimization problems to capture the different objectives of GRBs during non-service periods and grid-service periods, respectively. Then, we develop a physically consistent neural network (PCNN)-assisted hierarchical model predictive control (HMPC)-based GRB energy management algorithm to solve the optimization problem during non-service periods. Next, we propose a coordinated operation algorithm to solve the optimization problem during grid-service periods based on PCNN-assisted HMPC and rule-assisted binary search. By comparing the initial solutions from the proposed energy management algorithm with the final solutions generated by the proposed coordination algorithm, the demand changes of each GRB during service periods can be identified. Simulation results indicate that the proposed coordination algorithm achieves up to 37.8114% lower energy costs and 82.1459% better grid service performance than benchmarks while maintaining high thermal comfort. Note to Practitioners—Buildings with distributed energy resources (e.g., solar generation, energy storage) and flexible loads (e.g., heating, ventilation, and air conditioning (HVAC) systems) have significant potential to provide grid services, such as voltage support, frequency regulation, and power peak reduction. Since a single residential building contributes minimally to service quality, multi-building coordination through a trusted third party is necessary. However, since participation in providing grid services may affect occupant comfort and building energy costs, the demand change of each residential building should be identified so that the corresponding economic compensation can be calculated, which is a key factor for the successful deployment of such grid-interactive residential buildings (GRBs). To this end, we develop an optimal energy management algorithm for each residential building during non-service periods, which aims to minimize building energy cost while maintaining high occupant comfort. Based on the developed energy management algorithm, a coordinated operation algorithm for service periods is further proposed to limit the peak demand below a value predetermined by the system operator. By comparing the initial decisions from the energy management algorithm with the final decisions generated by the proposed coordination algorithm, we can identify the demand change of each building during service periods. Numerical results demonstrate that the proposed coordinated operation algorithm can help participating GRBs reduce energy costs and enhance service performance for power grids, with negligible sacrifice to occupant comfort.

Digitalization is one of the key drivers for energy system transformation. The advances in communication technologies and measurement devices render available a large amount of operational data and enable the centralization of such data storage and processing. The greater access to data opens up new opportunities for a more efficient and decentralized management of the energy system. At the distribution level of the energy system, local electricity markets (LEMs) provide new degrees of flexibility by trading and balancing the energy locally and offering ancillary services to the wider transmission and distribution system operators. Maximizing the grid impact from this flexibility calls for novel data analytics and artificial intelligence techniques to enhance the system’s security and reduce the energy costs of local prosumers. At the same time, however, relying on data-based approaches increases the risk of cyberattacks, and robust countermeasures are, therefore, needed as an integral aspect of digitalization efforts. This article discusses the key role of centralized data analytics to fully benefit from the advantages of LEMs in terms of system’s security enhancement and energy costs’ reduction. Data-driven paradigms are investigated that allow for flexibility from decentralized markets, mitigate the physical security risks, and devise defensive strategies shielding the system from cyber threats.

Multienergy microgrids (MEMGs) have significant potential to offer high energy utilization efficiency and system flexibility. The coordination of these MEMGs poses challenges due to the various system dynamics and uncertainties and the need to preserve privacy. This article proposes a double auction (DA)-market-based coordination framework. As such, MEMGs can not only schedule their own energy components but also trade energy with others in the DA market. After that, we formulate this problem as Markov games and propose a multiagent reinforcement learning method by making use of the DA market public information to enhance the stability with privacy perseverance. Case studies involving a real-world scenario validate the superior performance of the proposed method in reducing both the energy costs and the carbon emissions.

The deployment of renewable energy in power systems may raise serious voltage instabilities. Electric vehicles (EVs), owing to their mobility and flexibility characteristics, can provide various ancillary services including active and reactive power. However, the distributed control of EVs under such scenarios is a complex decision-making problem with enormous dynamics and uncertainties. Most existing literature employs model-based approaches to formulate active and reactive power control problems, which require full models and are time-consuming. This article proposes a multiagent reinforcement learning algorithm featuring a deep deterministic policy gradient (DDPG) method and a parameter sharing framework to solve the EVs’ coordinated active and reactive power control problem toward both demand-side response and voltage regulations. The proposed algorithm can further enhance the learning stability and scalability with privacy perseverance via the location marginal prices. Simulation results based on a modified IEEE 15-bus network are developed to validate its effectiveness in providing system charging and voltage regulation services. The proposed location marginal price (LMP) PSDDPG algorithm is evaluated to achieve 38%, 16%, and 25% speedup, and 1.58, 0.69, and 0.27 times higher reward over the benchmarks DDPG, TD3, and LMP-DDPG, respectively.

In responseto low-carbon requirements, a large amount of renewable energy resources (RESs) have been deployed in power systems; nevertheless, the intermittency of RESs raises the system vulnerability and even causes severe damage under extreme events. Electric vehicles (EVs), owing to their mobility and flexibility characteristics, can provide various ancillary services meanwhile enhancing system resilience. The distributed control of EVs under such scenarios in power-transportation network becomes a complex decision-making problem with enormous dynamics and uncertainties. To this end, a multiagent reinforcement learning method is proposed to compute both discrete and continuous actions simultaneously that aligns with the nature of EV routing and scheduling problems. Furthermore, the proposed method can enhance the learning stability and scalability with privacy perseverance in the multiagent setting. Simulation results based on IEEE 6- and 33-bus power networks integrated with transportation systems validate its effectiveness in providing system resilience and carbon intensity service.

With increasing prosumers employed with distributed energy resources (DER), advanced energy management has become increasingly important. To this end, integrating demand-side DER into electricity market is a trend for future smart grids. The double-side auction (DA) market is viewed as a promising peer-to-peer (P2P) energy trading mechanism that enables interactions among prosumers in a distributed manner. To achieve the maximum profit in a dynamic electricity market, prosumers act as price makers to simultaneously optimize their operations and trading strategies. However, the traditional DA market is difficult to be explicitly modelled due to its complex clearing algorithm and the stochastic bidding behaviors of the participants. For this reason, in this paper we model this task as a multi-agent reinforcement learning (MARL) problem and propose an algorithm called DA-MADDPG that is modified based on MADDPG by abstracting the other agents’ observations and actions through the DA market public information for each agent’s critic. The experiments show that 1) prosumers obtain more economic benefits in P2P energy trading w.r.t. the conventional electricity market independently trading with the utility company; and 2) DA-MADDPG performs better than the traditional Zero Intelligence (ZI) strategy and the other MARL algorithms, e.g., IQL, IDDPG, IPPO and MADDPG.

Peer-to-peer (P2P) energy trading has emerged as a promising market paradigm toward maximizing the value of distributed energy resources (DERs) 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 article 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.

The deployment of smart hybrid heat pumps (SHHPs) can introduce considerable benefits to electricity systems via smart switching between electricity and gas while minimizing the total heating cost for each individual customer. In particular, the fully optimized control technology can provide flexible heat that redistributes the heat demand across time for improving the utilization of low-carbon generation and enhancing the overall energy efficiency of the heating system. To this end, an accurate quantification of the preheating is of great importance to characterize the flexible heat. This paper proposes a novel data-driven preheating quantification method to estimate the capability of the heat pump demand shifting and isolate the effect of interventions. Varieties of fine-grained data from a real-world trial are exploited to estimate the baseline heat demand using Bayesian deep learning while jointly considering epistemic and aleatoric uncertainties. A comprehensive range of case studies are carried out to demonstrate the superior performance of the proposed quantification method, and then, the estimated demand shift is used as an input into the whole-system model to investigate the system implications and quantify the range of benefits of rolling out the SHHPs developed by PassivSystems to the future GB electricity systems.

The broker mechanism is widely applied to serve for interested parties to derive long-term policies in order to reduce costs or gain profits in smart grid. However, a broker is faced with a number of challenging problems such as balancing demand and supply from customers and competing with other coexisting brokers to maximize its profit. In this paper, we develop an effective pricing strategy for brokers in local electricity retail market based on recurrent deep multiagent reinforcement learning and sequential clustering. We use real household electricity consumption data to simulate the retail market for evaluating our strategy. The experiments demonstrate the superior performance of the proposed pricing strategy and highlight the effectiveness of our reward shaping mechanism.

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.

High penetration of renewable generation will increase the requirement for both operating reserve (OR) and frequency response (FR), due to its variability, uncertainty, and limited inertia capability. Although the importance of optimal scheduling of OR has been widely studied, the scheduling of FR has not yet been fully investigated. In this context, this paper proposes a computationally efficient mixed integer linear programming formulation for a full stochastic scheduling model that simultaneously optimizes energy production, OR, FR, and underfrequency load shedding. By using value of lost load (VOLL) as the single security measure, the model optimally balances the cost associated with the provision of various ancillary services against the benefit of reduced cost of load curtailment. The proposed model is applied in a 2030-GB system to demonstrate its effectiveness. The impact of installed capacity of wind generation and setting of VOLL is also analyzed.