A sequential hybridization of ETLBO and IPSO for solving reserve‐constrained combined heat, power and economic dispatch problem

Allam M., Nandhini M.

Feature selection is a significant task in the workflow of predictive modeling for data analysis. Recent advanced feature selection methods are using the power of optimization algorithms for choosing a subset of relevant features to get better classification results. Most of the optimization algorithms like genetic algorithm use many controlling parameters which need to be tuned for better performance. Tuning these parameter values is a challenging task for the feature selection process. In this paper, we have developed a new wrapper-based feature selection method called binary teaching learning based optimization (FS-BTLBO) algorithm which needs only common controlling parameters like population size, and a number of generations to obtain a subset of optimal features from the dataset. We have used different classifiers as an objective function to compute the fitness of individuals for evaluating the efficiency of the proposed system. The results have proven that FS-BTLBO produces higher accuracy with a minimal number of features on Wisconsin diagnosis breast cancer (WDBC) data set to classify malignant and benign tumors.

Goudarzi A., Zhang C., Fahad S., Mahdi A.J.

The ever-growing trend of electricity demand and environmental concerns have mandated the operation of electrical energy grids in a more economical and environmentally friendly manner. In the past few years, the integration of combined heat and power units has offered a promising solution to these concerns, however, at the same time a new challenging problem has revealed itself that is finding a simultaneous optimal solution between two competing criteria of power and heat. Furthermore, this problem will be more complex when the reduction of emission gasses is taken into consideration. Thus, to solve optimal scheduling of combined heat and power units, this study proposes an intelligent sequential algorithm based on the hybridization of teaching and learning-based optimization algorithm and an improved version of particle swarm optimization. The proposed algorithm is uniquely capable of the concurrent minimization of total generation costs and multi-pollutant gasses while several physical, operational, and environmental constraints are considered. Also, to ensure the safe maintenance of systems’ constraints, this study employs an adaptive violation constraint handling technique in conjunction with the proposed hybridized optimization algorithm. Finally, the performance of the proposed algorithm is compared to the recently developed methods, in which the proposed algorithm of the study outperforms all the other algorithms and achieves up to 2.2% lower overall costs of operation in most of the studied cases. • A sequential metaheuristic algorithm based on hybridization of TLBO and IPSO is proposed. • An adaptive penalization constraint handling technique is designed. • A pragmatic method for solving CHPEED problem model is formulated. • Several price penalty factors are developed to incorporate the cost of emission gasses in CHPEED problem.

Goudarzi A., Li Y., Fahad S., Xiang J.

• An enthusiasm aided TLBO algorithm for non-convex optimization is proposed. • An intelligent multi-criteria security-constrained optimal scheduling formulation is developed. • A self-adaptive power violation constraint (PVC) handling technique is implemented. • A game-theoretic DRP through dynamic pricing for cost-benefit analysis of customers and utility is designed. • New optimal pricing mechanisms are introduced in regulated and deregulated electricity markets. The advent of smart grid technologies due to the explosive increase in the electricity demand, has necessitated utilities around the globe in establishing intelligent demand response programs (DRPs) to influence customers' consumption patterns. The successful implementation of DRPs together with dynamic pricing strategies not only reduces energy prices in electricity markets but also improves network reliability and overall system efficiency. This study proposes a game theory-based DRP (GTDRP) which merges the incentive- and price-based DRP concepts with a focus on residential, commercial, and industrial sectors. Three pricing strategies are structured and compared, that is, fixed pricing, time-of-use pricing (for both utility- and customer-side), and real-time pricing along with their combination. Also, an enthusiasm-aided teaching and the learning-based optimization algorithm are developed to solve the GTDRP model through a self-adaptive power violation criterion. To validate the practicality of the presented model, three case studies through seven different scenarios are investigated. Results of the case studies demonstrate that the formulated multi-criteria security-constrained GTDRP can create a win-win situation for the utility and customers in the electricity markets, such that the utility profits increase, the customers' related costs reduce, and the load curve is flattened.

Deng L., Sun H., Li B., Sun Y., Yang T., Zhang X.

Combined heat and electricity operation with variable mass flow rates promotes flexibility, economy, and sustainability through synergies between electric power systems (EPSs) and district heating systems (DHSs). Such combined operation presents a highly nonlinear and nonconvex optimization problem, mainly due to the bilinear terms in the heat flow model—that is, the product of the mass flow rate and the nodal temperature. Existing methods, such as nonlinear optimization, generalized Benders decomposition, and convex relaxation, still present challenges in achieving a satisfactory performance in terms of solution quality and computational efficiency. To resolve this problem, we herein first reformulate the district heating network model through an equivalent transformation and variable substitution. The reformulated model has only one set of nonconvex constraints with reduced bilinear terms, and the remaining constraints are linear. Such a reformulation not only ensures optimality, but also accelerates the solving process. To relax the remaining bilinear constraints, we then apply McCormick envelopes and obtain an objective lower bound of the reformulated model. To improve the quality of the McCormick relaxation, we employ a piecewise McCormick technique that partitions the domain of one of the variables of the bilinear terms into several disjoint regions in order to derive strengthened lower and upper bounds of the partitioned variables. We propose a heuristic tightening method to further constrict the strengthened bounds derived from the piecewise McCormick technique and recover a nearby feasible solution. Case studies show that, compared with the interior point method and the method implemented in a global bilinear solver, the proposed tightening McCormick method quickly solves the heat–electricity operation problem with an acceptable feasibility check and optimality.

Tan H., Liu H., Yan W.

The combined heat and power system under variable flow control strategy has flexible supply heat regulation performance. However, the no n-convex and non-linear energy flow model of the heating network in this mode brings a huge challenge to the solution of the system scheduling model. To address this problem, a linear heating network energy flow model is first derived based on the assumption that the heating temperatures at load nodes are very small differences. Then, a first-order thermal load model considering the equivalent load generated by the fresh air system and the thermal inertia of the building is established according to the heat conduction process of the building and Fourier's law. Finally, a joint dispatch model for combined heat and power systems under variable flow control strategy is established. The validity of the model is verified by a 6-bus electric power system (EPS) and 6-node district heating network (DHN).

Wang X., Bie Z., Liu F., Kou Y.

The rapid growth of combined heat and power (CHP) units has led to the development of integrated electricity and district heating systems (IEHS). To support the design of a highly efficient energy supply system, this paper proposes a long-term co-optimization planning model for an IEHS. Not only CHP units, non-CHP thermal generators, wind farms and electric boilers but also transmission lines and heat pipelines are considered as investment candidates to meet electricity and heat demands. Nonlinear hydraulic conditions and thermal conditions are adopted to precisely capture the characteristics of the heating system. To make the planning model tractable, the nonlinear hydraulic conditions are approximated through piecewise linearization. Based on the introduction of auxiliary variables, the nonconvex thermal conditions are reformulated into linear constraints through quadratic convex relaxation. Hence, the planning model is converted into a large-scale mixed integer linear programming (MILP) problem. Since the planning model is formulated based on independent load blocks, a parallel Benders decomposition algorithm combined with the sequential bound-tightening procedure is proposed to efficiently obtain high-quality solutions. Numerical cases are studied based on two IEHSs of different scales to validate the effectiveness of the proposed co-optimization planning model and the feasibility of the proposed solution methods for solving this complicated planning model for an IEHS.

Jiang Y., Wan C., Botterud A., Song Y., Shahidehpour M.

Combined heat, and power dispatch promotes interactions, and synergies between electric power systems, and district heating systems. However, nonlinear, and nonconvex heating flow imposes significant challenges on finding qualified solutions efficiently. Most existing methods rely on constant flow assumptions to derive a linear heating flow model, sacrificing optimality for computational simplicity. This paper proposes a novel convex combined heat, and power dispatch model based on model simplification, and constraint relaxations, which improves solution quality, and avoids assumptions on operating regimes of district heating systems. To alleviate mathematical complexity introduced by the commonly used node method, a simplified thermal dynamic model is proposed to capture temperature changes in networked pipelines. Quadratic, and polyhedral relaxations are then applied to convexify the original problem with quadratic equality, and bilinear constraints. Furthermore, an adaptive solution algorithm is developed to successively reduce the relaxation area based on sequential bound tightening, which improves solution optimality with desirable computational efficiency. The proposed method is verified on a distribution-level, and a transmission-level integrated electricity, and heat systems, compared to constant-flow-based solutions, and iterative algorithms.

Goudarzi A., Li Y., Xiang J.

In the era of the fast-growing green energy technologies, microgrid (MG) systems with integrated renewable energy sources (RESs) such as solar and wind are becoming more attractive and ubiquitous. Furthermore, concerning the uncertain power production nature of the RESs, battery storage systems have turn out to be an inseparable part of MGs, while they improve the reliability, efficiency, and operating cost of the entire system. In such an intricate situation, optimal utilization of RESs is substantially dependent on the employed energy management system (EMS). Also, mitigation of fossil fuel generators that results in the reduction of multipollutant gasses hinges on the competency of EMS. This chapter presents an improved teaching- and learning-based optimization (ITLBO) method for the energy management of MGs in the islanding and grid-connected modes where the MG conducts a business trade-off with the main grid. To handle the uncertain behavior of the intermittent generation resources, the study has exploited a triple exponential smoothing (TES) method to predict their erratic power production level on a day-ahead basis. The proposed EMS is tested on several scenarios, and the simulation results are compared with other optimization algorithms. The obtained results demonstrate a noticeable improvement with the proposed method.

Chen X., Li K., Xu B., Yang Z.

Combined heat and power economic dispatch (CHPED) is an important optimization task in the economic operation of power systems. The interdependence of heat and power outputs of cogeneration units and valve-point effects of thermal units impose non-convexity, nonlinearity and complication in the dispatch modeling and optimization. In this paper, a novel PSO algorithm called biogeography-based learning particle swarm optimization (BLPSO) is applied to solve the CHPED problem considering various constraints including power output balance, heat production balance, feasible operation area of cogeneration unit and prohibited operation zones. In BLPSO, based on a biogeography-based learning model, each particle uses a migration operator to update itself based on the personal best position of all particles. This updating strategy helps BLPSO overcome premature convergence and improve solution accuracy. Moreover, a repair technique is employed to handle the system constraints and guide the solutions toward feasible zones. The effectiveness of the proposed method is evaluated by testing on four CHPED problems containing 5, 7, 24, and 48 units. The experimental results show that BLPSO outperforms the state-of-the-art methods in terms of solution accuracy and stability. Therefore, BLPSO can be regarded as a promising alternative for the CHPED problem. • BLPSO algorithm is applied for solving CHPED problem with various constraints. • The interdependence of heat and power outputs of cogeneration units impose great complication. • Non-convex CHPED problems with/without prohibited operating zones are considered. • Comprehensive simulation results demonstrate the effectiveness of the BLPSO algorithm.

Mellal M.A., Williams E.J.

This paper is related to a solution approach for the nonlinear and nonconvex combined heat and power economic dispatch problem (CHPED). It combines the cuckoo optimization algorithm with penalty function (PFCOA) published in “Mellal and Williams (2015)” and the binary approach published in “Geem and Cho (2012).” The binary approach discretizes the nonconvex operating feasible region into two convex regions in order to explore the whole operating region. A numerical case study involving four units is investigated and the superiority of the mixed method, i.e, the PFCOA with the binary approach is proved.

Srivastava A., Das D.K.

In this article, a new optimization technique known as Kho-Kho optimization (KKO) algorithm is presented. This proposed technique is a population based meta-heuristic method which is inspired from the strategies used by players in a well known tag-team game played in India, i.e. Kho-Kho. The performance and superiority of the proposed method with respect to other existing methods is evaluated using twenty nine benchmark functions and real-time optimization problems related to power system i.e. combined emission economic dispatch and combined heat and power economic dispatch problem.

Zhou S., Hu Z., Gu W., Jiang M., Chen M., Hong Q., Booth C.

This paper proposed a Deep Reinforcement learning (DRL) approach for Combined Heat and Power (CHP) system economic dispatch which obtain adaptability for different operating scenarios and significantly decrease the computational complexity without affecting accuracy. In the respect of problem description, a vast of Combined Heat and Power (CHP) economic dispatch problems are modeled as a high-dimensional and non-smooth objective function with a large number of non-linear constraints for which powerful optimization algorithms and considerable time are required to solve it. In order to reduce the solution time, most engineering applications choose to linearize the optimization target and devices model. To avoid complicated linearization process, this paper models CHP economic dispatch problems as Markov Decision Process (MDP) that making the model highly encapsulated to preserve the input and output characteristics of various devices. Furthermore, we improve an advanced deep reinforcement learning algorithm: distributed proximal policy optimization (DPPO), to make it applicable to CHP economic dispatch problem. Based on this algorithm, the agent will be trained to explore optimal dispatch strategies for different operation scenarios and respond to system emergencies efficiently. In the utility phase, the trained agent will generate optimal control strategy in real time based on current system state. Compared with existing optimization methods, advantages of DRL methods are mainly reflected in the following three aspects: 1) Adaptability: under the premise of the same network topology, the trained agent can handle the economic scheduling problem in various operating scenarios without recalculation. 2) High encapsulation: The user only needs to input the operating state to get the control strategy, while the optimization algorithm needs to re-write the constraints and other formulas for different situations. 3) Time scale flexibility: It can be applied to both the day-ahead optimized scheduling and the real-time control. The proposed method is applied to two test system with different characteristics. The results demonstrate that the DRL method could handle with varieties of operating situations while get better optimization performance than most of other algorithms.

Sadeghian O., Moradzadeh A., Mohammadi-Ivatloo B., Abapour M., Garcia Marquez F.P.

Yearly generation maintenance scheduling (GMS) of generation units is important in each system such as combined heat and power (CHP)-based systems to decrease sudden failures and premature degradation of units. Imposing repair costs and reliability deterioration of system are the consequences of ignoring the GMS program. In this regard, this research accomplishes GMS inside CHP-based systems in order to determine the optimal intervals for predetermined maintenance required duration of CHPs and other units. In this paper, cost minimization is targeted, and violation of units’ technical constraints like feasible operation region of CHPs and power/heat demand balances are avoided by considering related constraints. Demand-response-based short-term generation scheduling is accomplished in this paper considering the maintenance intervals obtained in the long-term plan. Numerical simulation is performed and discussed in detail to evaluate the application of the suggested mixed-integer quadratic programming model that implemented in the General Algebraic Modeling System software package for optimization. Numerical simulation is performed to justify the model effectiveness. The results reveal that long-term maintenance scheduling considerably impacts short-term generation scheduling and total operation cost. Additionally, it is found that the demand response is effective from the cost perspective and changes the generation schedule.

Sundaram A.

This study implements a potent Multiobjective Multi-Verse Optimization algorithm to solve the highly complicated combined economic emission dispatch and combined heat and power economic emission dispatch problems. Solving these problems operates the power system integrated with cogeneration plants economically and reduces the environmental impacts caused by the pollutants of fossil fuel-fired power plants. A chaotic opposition based strategy is proposed to explore the search space extensively and to generate the initial populations for the multiobjective optimization algorithm. An effective constraint handling mechanism is also proposed to enable the population to remain within the bounds and in the feasible operating region of the cogeneration plants. The algorithm is applied to standard test functions, four test systems including a large 140 bus system considering valve-point effects, ramp limits, transmission power losses, and the feasible operating region of cogeneration units. The Pareto Optimal solutions obtained by the algorithm are well spread and diverse when compared with other optimization algorithms. The statistical analysis and various performance metrics used indicate the algorithm converges to true POF and is a viable alternative to solve the highly complicated combined economic emission dispatch and combined heat and power economic emission dispatch problems.

Goudarzi A., Li Y., Xiang J.

Fossil-fuel based power sources cause environmental pollution such as the degradation of air quality and climate change, which negatively impacts the life on the earth. Consequently, this demands that the power generation should consider the optimal management of thermal sources that are aimed at minimizing the emission of gasses in the generation mix. The production volume of multi-pollutant gasses (SO2, NOx, and CO2) can be reduced through a combined environmental economic dispatch (CEED) approach. This study has proposed a hybrid algorithm based on a novel combination of a modified genetic algorithm and an improved version of particle swarm optimization abbreviated as MGAIPSO to solve CEED problem. The study utilizes three robust operators to enhance the performance of the proposed hybrid algorithm. In GA, a uniformly weighted arithmetic crossover and a normally distributed mutation operator have been implemented to produce elite off-springs in each iteration and diversify the solutions in the search space. In the case of PSO, a non-linear time-varying double-weighted (NLTVDW) technique is developed to obtain a substantial balance between exploration and exploitation. To further enhance the exploitation ability of the MGAIPSO, this study has implemented two movements correctional methods to continuously monitor and amend the position and velocity of the particles. Several numerical case studies ranging from small to large-scale are carried out to validate the practicality of the proposed algorithm.

Zhang L., Zhang H.

Abstract With the increasingly serious global environmental and energy issues, more countries are using environmental and economic dispatch (EED) models to optimize power systems. To better optimize the power system, an adaptive artificial bee colony (ABC) algorithm based on memory feedback mechanism was put forward to address the EED model, and the adaptive algorithm was used to adaptively adjust the population size. The study also used a benchmark function to set an appropriate population size. In addition, the study also considered both fuel cost and environmental factors in the model, and simultaneously considered four constraint conditions. To evidence the function of the adaptive algorithm, different algorithms were compared in the study. The outcomes denoted that the minimum values of the optimal solution under the Sphere function, Matyas function, and Dixon Price function were 1 × 10−273, 1 × 10−162, and 1 × 10−16, respectively, and their corresponding population sizes were 7, 18, and 20. Under the Sphere function, the minimum average fitness values of the algorithm designed by the research, the ABC algorithm, and the current optimal ABC algorithm were 10−15, 10−4, and 10−11, respectively. Moreover, the algorithm designed by the research tended to flatten out after nearly 30 iterations. The total cost of the adaptive algorithm, ABC algorithm, and the optimal algorithm was 102126.0573 yuan, 113001.0383 yuan, and 109594.9634 yuan, respectively. The pollutant emissions of the three algorithms were 1246.1048 yuan, 1250.5744 yuan, and 1344.3922 yuan, respectively. The adaptive algorithm based on memory feedback mechanism had obvious advantages in solving EED models. The adaptive algorithm proposed by the research achieved adaptive adjustment of population size, improved the operational efficiency of the algorithm, and had certain reference significance for solving other problems.

El-Afifi M.I., Sedhom B.E., Padmanaban S., Eladl A.A.

Khan M.A., Khan T., Waseem M., Saleh A.M., Qamar N., Muqeet H.A.

AbstractSignificant attempts have been made to make the electrical grid more intelligent and responsive to better meet customers' requirements while boosting the stability and efficiency of current power systems. Smart grid technologies, which have just recently emerged, facilitated the incorporation of demand response (DR) by introducing an information and communication backbone to the current system. The Internet of Things (IoT) has emerged as a key technology for smart energy grids. Security concerns have emerged as a major obstacle to the widespread adoption of IoT‐enabled devices because of the inherent Internet connectivity of these smart gadgets. Therefore, security is a crucial factor to address before the widespread implementation of IoT‐based devices in power grids. In this study, the framework and architecture of smart grids that are enabled by the IoT are first examined. Then, the role of IoT for DR in smart grids and different approaches adopted worldwide to make DR schemes more effective, have been discussed in detail. Finally, the authors discuss how IoT‐enabled smart grids can benefit from cutting‐edge solutions and technologies that make them more secure and resistant to cyber and physical attacks.

Hasanabadi R., Sharifzadeh H.

Combined heat and power economic dispatch (CHPED) can enhance energy efficiency compared with conventional economic dispatch (ED). From the optimization standpoint, the CHPED problem usually involves the nonlinear products of heat and power generation variables, nonconvex objective functions, and nonconvex feasible operating range. Thus, its solution method should be able to cope with the problematic nonconvex problem since finding a poor solution for the CHPED implies reducing the maximum achievable efficiency. This paper presents an effective method utilizing several mathematical transformations to cope with the nonlinear, nonconvex terms. The method transforms the nonconvex regions and nonlinear functions into convex polyhedrons and segments. Then, the method formulates the polyhedrons and segments with integer variables, logical constraints, and combinatorial restrictions. Thus, we derive a mixed integer model, which optimization software can better solve. Simulation results illustrate the effectiveness of the method presented and its advantages compared with existing CHPED solution techniques in the literature.

Singh N., Chakrabarti T., Chakrabarti P., Panchenko V., Budnikov D., Yudaev I., Bolshev V.

Thermal power plants use coal as a fuel to create electricity while wasting a significant amount of energy as heat. If the heat and power plants are combined and used in cogeneration systems, it is possible to reuse the waste heat and hence enhance the overall efficiency of the power plant. In order to minimize production costs while taking system constraints into account, it is important to find out the optimal operating point of power and heat for each unit. Combined heat and power production is now widely used to improve thermal efficiency, lower environmental emissions, and reduce power generation costs. In order to determine the best solutions to the combined heat and power economic dispatch problem, several traditional as well as innovative heuristic optimization approaches were employed. This study offers a thorough analysis of the use of heuristic optimization techniques for the solution of the combined heat and power economic dispatch problem. In this proposed work, the most well-known heuristic optimization methods are examined and used for the solution of various generating unit systems, such as 4, 7, 11, 24, 48, 84, and 96, taking into account various constraints. This study analyzes how various evolutionary approaches are performed for various test systems. The heuristic methodologies’ best outcomes for various case studies with restrictions are contrasted.

Deng L., Chen X., Dong X., Liu X., Zhang X.

Hassan M.H., Kamel S., Shaikh M.S., Alquthami T., Hussien A.G.

AbstractThe Economic and Emission Dispatch (EED) method is widely used to optimize generator output in a power system. The goal is to reduce fuel costs and emissions, including carbon dioxide, sulphur dioxide, and nitrogen oxides, while maintaining power balance and adhering to limit constraints. EED aims to minimize emissions and operating costs while meeting power demands. To solve the multi‐objective EED problem, the supply‐demand optimization (SDO) algorithm is proposed, which employs a price penalty factor approach to convert it into a single‐objective function. The SDO algorithm uses a swarm‐based optimization strategy inspired by supply‐demand mechanisms in economics. The algorithm's performance is evaluated on seven benchmark functions before being used to simulate the EED problem on power systems with varying numbers of units and load demands. Established algorithms like the Grey Wolf Optimizer (GWO), Moth‐Flame Optimization (MFO), Transient Search Optimization (TSO), and Whale Optimization Algorithm (WOA) are compared to the SDO algorithm. The simulations are conducted on power systems with different numbers of units and load demands to optimize power generation output. The numerical analyses demonstrate that the SDO technique is more efficient and produces higher quality solutions than other recent optimization methods.

Kaur P., Chaturvedi K.T., Kolhe M.L.

Combined heat and power (CHP) plants have opportunities to work as distributed power generation for providing heat and power demand. Furthermore, CHP plants contribute effectively to overcoming the intermittence of renewable energy sources as well as load dynamics. CHP plants need optimal solution(s) for providing electrical and heat energy demand simultaneously within the smart network environment. CHP or cogeneration plant operations need appropriate techno-economic dispatching of combined heat and power with minimising produced energy cost. The interrelationship between heat and power development in a CHP unit, the valve point loading effect, and forbidden working regions of a thermal power plant make the CHP economic dispatch’s (CHPED) objective function discontinuous. It adds complexity in the CHPED optimisation process. The key objective of the CHPED is operating cost minimisation while meeting the desired power and heat demand. To optimise the dispatch operation, three different algorithms, like Jaya algorithm, Rao 3 algorithm, and hybrid CHPED algorithm (based on first two) are adopted containing different equality and inequality restrictions of generating units. The hybrid CHPED algorithm is developed by the authors, and it can handle all of the constraints. The success of the suggested algorithms is assessed on two test systems; 5-units and 24-unit power plants.

Waseem M., Adnan Khan M., Goudarzi A., Fahad S., Sajjad I.A., Siano P.

Smart grid integrates computer, communication, and sensing technologies into existing power grid networks to achieve significant informatization-related advantages. It will provide communication between neighbors, localized management, bidirectional power transfer, and effective demand response. Smart grids (SG) replace conventional grids by integrating various operational measures, including smart automation appliances, smart meters, and renewable energy sources. Regarding energy management and resolving energy issues, SG is one of the most cutting-edge and potentially game-changing innovations. Even still, its complexity suggests that decentralization may provide significant gains. Because of its increasing digitization and interconnectedness, it is also vulnerable to cyber threats. Blockchain, in this sense, is a potential SG paradigm solution that provides several great benefits. Even though blockchains have been widely discussed to decentralize and strengthen smart grid cybersecurity, they have not yet been researched in depth from an application and architectural standpoint. Blockchain-enabled SG applications are the subject of an in-depth research investigation. Electric vehicles (EVs), home automation, energy management systems, etc., are only a few of the many examples that have prompted the proposal of blockchain designs for their respective use cases. Information communication network security is of paramount importance. However, this evolving system raises cybersecurity issues. This paper aims to guide researchers in the right manner so they may build blockchain-based, secure, distributed SG applications in the future. This article also summarizes cybersecurity threats pertaining to smart grids. It starts with a description of a blockchain followed by the blockchain infrastructure, challenges, and solutions for different smart grid applications. A look back at the tried-and-true methods of securing a power grid is offered, and then it discusses the newer and more complex cybersecurity threats to the smart grid. In addition, models of common cyberattacks are presented, and the methods of defense against them are examined.

Goudarzi A., Ghayoor F., Waseem M., Fahad S., Traore I.

Swift population growth and rising demand for energy in the 21st century have resulted in considerable efforts to make the electrical grid more intelligent and responsive to accommodate consumers’ needs better while enhancing the reliability and efficiency of modern power systems. Internet of Things (IoT) has appeared as one of the enabling technologies for smart energy grids by delivering abundant cutting-edge solutions in various domains, including critical infrastructures. As IoT-enabled devices continue to flourish, one of the major challenges is security issues, since IoT devices are connected through the Internet, thus making the smart grids vulnerable to a diverse range of cyberattacks. Given the possible cascading consequences of shutting down a power system, a cyberattack on a smart grid would have disastrous implications for the stability of all grid-connected infrastructures. Most of the gadgets in our homes, workplaces, hospitals, and on trains require electricity to run. Therefore, the entire grid is subject to cyberattacks when a single device is hacked. Such attacks on power supplies may bring entire cities to a standstill, resulting in massive economic losses. As a result, security is an important element to address before the large-scale deployment of IoT-based devices in energy systems. In this report, first, we review the architecture and infrastructure of IoT-enabled smart grids; then, we focus on major challenges and security issues regarding their implementation. Lastly, as the main outcome of this study, we highlight the advanced solutions and technologies that can help IoT-enabled smart grids be more resilient and secure in overcoming existing cyber and physical attacks. In this regard, in the future, the broad implementation of cutting-edge secure and data transmission systems based on blockchain techniques is necessary to safeguard the entire electrical grid against cyber-physical adversaries.