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The smart grid is enabling the collection of massive amounts of high-dimensional and multi-type data about the electric power grid operations, by integrating advanced metering infrastructure, control technologies, and communication technologies. However, the traditional modeling, optimization, and control technologies have many limitations in processing the data; thus, the applications of artificial intelligence (AI) techniques in the smart grid are becoming more apparent. This survey presents a structured review of the existing research into some common AI techniques applied to load forecasting, power grid stability assessment, faults detection, and security problems in the smart grid and power systems. It also provides further research challenges for applying AI technologies to realize truly smart grid systems. Finally, this survey presents opportunities of applying AI to smart grid problems. The paper concludes that the applications of AI techniques can enhance and improve the reliability and resilience of smart grid systems.

Difference methods are widely used for the approximate solution of boundary value problems for partial differential equations. Grid approximations are most simply constructed when the computational domain is divided into rectangular cells. Typically, the grid nodes coincide with the vertices of the cells. In addition to such node-center approximations, grids with nodes at the centers of cells are also used. It is convenient to formulate boundary value problems in terms of invariant operators of vector (tensor) analysis, which are associated with corresponding grid analogs. In this work, analogs of the gradient and divergence operators are constructed on non-standard rectangular grids the nodes of which consist of both the vertices of the computational cells and their centers. The proposed approach is illustrated using approximations of a boundary value problem for a stationary two-dimensional convection–diffusion equation. The key features of constructing approximations for vector problems are discussed with a focus on applied problems of the mechanics of solids.

The development of wind-and solar-generating capacity is growing rapidly around the world as policy makers pursue various energy policy objectives. This paper will describe the challenges in integrating wind and solar generation, the lessons learned, and recommended strategies from both operating experience and integration studies. Case studies on the experience with wind and solar integration in China, Germany, and Spain are also included in this paper. The paper is organized as follows. First section summarizes worldwide wind and solar development, the challenges in integrating wind and solar generation, and some of the lessons learned from studies designed to evaluate the impact of higher levels of wind and solar generation and also from the operational experience in some countries with larger amounts of renewable energy. The second section summarizes some of the solutions for incorporating higher levels of wind and solar capacity into short-term system operations. This section also explains basic methodologies to implement system operations studies to understand the impacts of variability in system operation. The third section explains the contribution of variable renewables to long-term supply adequacy-commonly called 'firm' power-and the relationship of this to long-term reserves; it also explores how these issues can be incorporated into long-term planning or adequacy assessments. Overall, the variability of wind power generation adds to the variability on the grid in most time scales, and a key question that wind integration studies must address is whether there is enough existing capability on the grid to manage that increased variability, or whether new sources, such as new generation or increased levels of demand response, must be added to manage that variability.

Electricity is crucial for daily life due to the number of activities that depend on it. To forecast future electric load, which changes over time and depends on various factors, grid operators (GOs) must create forecasting models for various time horizons with a high degree of accuracy because the results have a huge impact on their decision-making regarding, for example, the scheduling of power units to supply user consumption in the short or long term or the installation of new power plants. This has led to the exploration of multiple techniques like statistical models and Artificial Intelligence (AI), with Machine-Learning and Deep-Learning algorithms being the most popular in this latter field. This paper proposes a neural network-based model to forecast short-term load for a Colombian grid operator, considering a seven-day time horizon and using an LSTM recurrent neural network with historical load values from a region in Colombia and calendar features such as holidays and the current month corresponding to the target week. Unlike other LSTM implementations found in the literature, in this work, the LSTM cells read multiple load measurements at once, and the additional information (holidays and current month) is concatenated to the output of the LSTM. The result is used to feed a fully connected neural network to obtain the desired forecast. Due to social problems in the country, the load data presents a strange behavior, which, in principle, affects the prediction capacity of the model. Still, it is eventually able to adjust its forecasts accordingly. The regression metric MAPE measures the model performance, with the best predicted week having an error of 1.65% and the worst week having an error of 26.22%. Additionally, prediction intervals are estimated using bootstrapping.

With the multidisciplinary complexity of resilience challenges, holistic evaluation and enhancement have been the main concerns in resilience research. This paper addresses this gap by demonstrating the integrated analysis of operator response capacity, energy policy support and infrastructure robustness using Malawi’s cases. The three individual case studies were pooled, focusing on integrating their resilience indicators, identifying the resilience weaknesses and mapping their interdependencies to inform holistic integrated, holistic enhancement measures. A nuanced understanding of resilience was achieved by integrating indicators based on their respective capacities (preventive and anticipative, absorptive, adaptive, restorative and transformative) across the three resilience dimensions (operator, policy and infrastructure). Mapping relationships between indicators revealed crucial interdependencies and was essential for understanding the complex relationships that underpin resilience. This resulted in development of an integrated resilience framework (IRF) which provides guidelines for comprehensive and inclusive resilience evaluations, especially for weak and underdeveloped grids. The structure of the electricity supply and institutional challenges are at the centre of Malawi’s resilience challenges, aggravated by the non-implementation of the energy policy, which results from, among other reasons, political interference and financial constraints. The paper provides integrated solutions to the identified resilience challenges.

Demand response programs can effectively handle the smart grid’s increasing energy demand and power imbalances. In this regard, price-based DR (PBDR) and incentive-based DR (IBDR) are two broad categories of demand response in which incentives for consumers are provided in IBDR to reduce their demand. This work aims to implement the IBDR strategy from the perspective of the service provider and consumers. The relationship between the different entities concerned is modelled. The incentives offered by the service provider (SP) to its consumers and the consumers’ reduced demand are optimized using Stackelberg–particle swarm optimization (SPSO) as a bi-level problem. Furthermore, the system with a grid operator, the industrial consumers of the grid operator, the service provider and its consumers are analyzed from the service provider’s viewpoint as a tri-level problem. The benefits offered by the service provider to its customers, the incentives provided by the grid operator to its industrial customers, the reduction of customer demand, and the average cost procured by the grid operator are optimized using SPSO and compared with the Stackelberg-distributed algorithm. The problem was analyzed for an hour and 24 h in the MATLAB environment. Besides this, sensitivity analysis and payment analysis were carried out in order to delve into the impact of the demand response program concerning the change in customer parameters.

Multigrid reduction in time (MGRIT), one of the most popular parallel-in-time approaches, extracts temporal parallelism by constructing coarse grids in the time direction. The coarse-grid operator optimization method for MGRIT has achieved high convergence for one of the hyperbolic problems that had poor convergence performance: the one-dimensional linear advection problems with constant coefficients. This paper applies this optimization method to two-dimensional linear time-dependent Stokes and Oseen problems using the pressure projection and the staggered grid discretization methods. Although the time-stepping operator involves the projection operator, the commutativity in the periodic boundary conditions allows a similar adaptation of the coarse-grid operator optimization for scalar equations. This method can also be applied to Dirichlet boundary problems by modifying the operator obtained based on the assumption of periodic boundary conditions. We demonstrate that MGRIT can achieve reasonable convergence rates for these problems with a practical number of non-zero elements by using the optimization method. Numerical experiments show convergence estimates for periodic boundary problems, applications to Dirichlet boundary problems, and parallel results compared to the sequential time-stepping method.

A hybrid energy system (HES) integrates various energy resources to attain synchronized energy output. However, HES faces significant challenges due to rising energy consumption, the expenses of using multiple sources, increased emissions due to non-renewable energy resources, etc. This study aims to develop an energy management strategy for distribution grids (DGs) by incorporating a hydrogen storage system (HSS) and demand-side management strategy (DSM), through the design of a multi-objective optimization technique. The primary focus is on optimizing operational costs and reducing pollution. These are approached as minimization problems, while also addressing the challenge of achieving a high penetration of renewable energy resources, framed as a maximization problem. The third objective function is introduced through the implementation of the demand-side management strategy, aiming to minimize the energy gap between initial demand and consumption. This DSM strategy is designed around consumers with three types of loads: sheddable loads, non-sheddable loads, and shiftable loads. To establish a bidirectional communication link between the grid and consumers by utilizing a distribution grid operator (DGO). Additionally, the uncertain behavior of wind, solar, and demand is modeled using probability distribution functions: Weibull for wind, PDF beta for solar, and Gaussian PDF for demand. To tackle this tri-objective optimization problem, this work proposes a hybrid approach that combines well-known techniques, namely, the non-dominated sorting genetic algorithm II and multi-objective particle swarm optimization (Hybrid-NSGA-II-MOPSO). Simulation results demonstrate the effectiveness of the proposed model in optimizing the tri-objective problem while considering various constraints.

This letter proposes a concept to discriminate an electrical fault from a cyber attack in the modern power system. A cyber attack factor is introduced which may mislead the bus voltage stability virtually at load buses. The proposed cyber attack models are validated by executing multiple cyber attacks at a time on Western system coordinating council (WSCC) 9 bus test power system by using Siemens PSS/E and MATLAB softwares. Further, the impact of electrical fault and cyber attack on the WSCC 9 bus test power systems voltage stability has been analysed to develop a discrimination algorithm in reference to chosen load index. Despite its simplicity, the proposed discrimination algorithm is robust, accurate and quite suitable to develop intelligent measures for mal-operations against cyber attacks in the smart electric grid.

Integrated energy system (IES) has received increasing attention in micro grid due to the high energy efficiency and low emission of carbon dioxide. Based on the technology of combined heat and power (CHP), this paper develops a novel operation mechanism with community micro turbine and shared energy storage system (ESS) for energy management of prosumers. In the proposed framework, micro-grid operator (MGO) equipped with micro turbine and ESS provides energy selling business and ESS leasing business for prosumers. Prosumers can make energy trading with public grid and MGO, and ESS will be shared among prosumers when they pay for the rent to MGO. Based on such framework, we adopt a cooperative game for prosumers to determine optimal energy trading strategies from MGO and public grid for the next day. Concretely, a cooperative game model is formulated to search the optimal strategies aiming at minimizing the daily cost of coalition, and then a bilateral Shapley value (BSV) is proposed to solve the allocation problem of coalition’s cost among prosumers. To verify the effectiveness of proposed energy management framework, a practical example is conducted with a community energy network containing MGO and 10 residential buildings. Simulation results show that the proposed scheme is able to provide financial benefits to all prosumers, while providing peak load leveling for the grid.