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The utilization of accurate models is crucial in the various stages of development for photovoltaic (PV) systems. Modelling these systems effectively allows developers to assess new modifications prior to the manufacturing phase, resulting in cost and time savings. This research paper presents a viable approach to accurately estimate both static and dynamic PV models. The proposed estimation method relies on a novel and enhanced optimization algorithm called leader artificial ecosystem‐based optimization (LAEO), which improves upon the original artificial ecosystem‐based optimization (AEO). The proposed LAEO algorithm integrates the adaptive probability (AP) and leader‐based mutation‐selection strategies to enhance the search capability, improve the balance between exploration and exploitation, and overcome local optima. To evaluate the effectiveness of LAEO, it was tested on 23 different benchmark functions. Additionally, LAEO was applied to estimate the parameters of static three‐diode PV models, as well as integral‐order and fractional‐order dynamic models. This paper showcases practical implementations of photovoltaic (PV) parameter estimation in various scenarios, including the static three‐diode model, dynamic integral order model (IOM), and fractional order model (FOM). The results were assessed from various angles to examine the precision, performance, and stability of the LAEO algorithm. [ABSTRACT FROM AUTHOR]

A machine learning‐based optimized droop method is suggested here to simultaneously reduce the production cost (PC) and power line losses (PLL) for a class of direct current (DC) microgrids (MGs). Traditionally, a communication‐less technique known as the hybrid droop method has been employed to decrease PC and PLL in DC MGs. However, achieving the desired reduction in either PC or PLL requires arbitrary adjustments of weighting coefficients for each distributed generator in the conventional hybrid droop method. To address this challenge, this paper introduces a systematic approach that capitalizes on the benefits of artificial intelligence to accurately predict both the PC and PLL in a DC MG. Furthermore, an optimization technique relying on the gradient descendent method is employed to independently optimize both PC and PLL for each scenario. The effectiveness of the proposed method is confirmed through a comparative study with classical and hybrid droop coordination schemes under various scenarios such as rapid load changes. [ABSTRACT FROM AUTHOR]

In distribution networks, among the planning problems, optimal placement of medium voltage to low voltage (MV/LV) transformers is a vital and challenging issue. Electrical load uncertainty is an important factor that affects the result of this planning problem. This paper investigates optimal allocation of service transformers with respect to the load uncertainty modelled by information gap decision theory (IGDT). For this aim, the planning problem is solved in risk‐neutral (RN) and risk‐averse (RA) frameworks. In RN strategy, objective function is defined to minimize the cost of service transformers and low voltage feeders as well as the cost of power losses. On the other hand, in RA strategy, objective function is defined to maximize the radius of the uncertainty in such a way that any deviation of the uncertain parameter results in an objective function value that is not worse than the critical limit. The optimization problem is solved by crow search algorithm (CSA) and particle swarm optimization (PSO) and the results are compared. In mid‐term planning, with respect to the deviation factors of 0.05, 0.1, 0.15, 0.2, 0.25 and 0.3, optimal values of the uncertainty radius are 5.89%, 13.64%, 21.37%, 28.97%, 34.39% and 43.46%, respectively. In long‐term planning, with respect to the deviation factors of 0.05, 0.1, 0.15, 0.2, 0.25 and 0.3, optimal values of the uncertainty radius are 6.92%, 13.33%, 20.39%, 27.03%, 34% and 40.46%, respectively. Moreover, on average, CSA finds more promising results than PSO. [ABSTRACT FROM AUTHOR]

A large‐scale AC/DC hybrid power network has recently been constructed in China because of the fast development of high voltage direct current (HVDC) technology. The receiving AC system must have sufficient system strength to guarantee the safe operation of the DC side. The paradox is that raising the AC side system's strength will increase the short‐circuit current (SCC) level, endangering the safety of the system. In order to resolve the contradiction between the system strength of multi‐infeed DC receiving‐end power networks and SCC, this paper proposes an optimal transmission switching strategy. Firstly, the system strength is characterised by the multi‐infeed short‐circuit ratio (MISCR), followed by constructing a comprehensive constraint set that considers the MISCR and SCC simultaneously. Secondly, to optimize SCC with MISCR and N−1 safety constraints, a mixed integer linear programming model is developed. Finally, to enhance the computing performance for large power systems, a new connectivity constraint set and decision space reduction techniques are provided. The proposed method is tested for effectiveness using the IEEE 30‐bus and IEEE 118‐bus systems. [ABSTRACT FROM AUTHOR]

Integrated hydrogen energy systems (IHESs) have become attractive alternatives to cope with the depletion of fossil fuels and increasingly severe climate change problems. This paper proposes an adaptively optimal energy scheduling method based on deep deterministic policy gradient (DDPG) to improve the operational efficiency of IHES. The optimal scheduling problem is formulated as a Markov decision process problem with action space, environmental states, and action‐value function. The DDPG‐based optimal energy management algorithm with actor‐critic structure is proposed based on policy gradients and neural networks. Through actor‐critic network training and policy iteration, the energy management scheme can be adaptively optimized according to the dynamic responses of IHES. The benefits of the proposed algorithm are analysed through time‐domain simulations, and the scheduling robustness under different uncertain conditions is verified. [ABSTRACT FROM AUTHOR]

With the emergence of the Smart Grid concept, utility companies require more active participation of home users in the power sector. This changing paradigm is enabled by the wide deployment of multiple home assets such as small renewable‐based generators or storage facilities. In this context, consumers are no longer conceived as pure loads but also active agents that can exchange energy with the grid. To promote this active participation, utility companies promote different price‐based demand response programs to change the consumer patterns on pursuing a more efficient and economic system operation. In this regard, home energy management programs are becoming an essential tool for efficiently managing the different home users while addressing multiple demand response goals at minimum cost. In essence, a home energy management system is a computational optimization tool, which has to handle multiple uncertainties brought by weather forecast or energy pricing. This paper tackles this issue by developing a novel robust home energy management program based on interval optimization. In contrast to other related approaches, the proposal avoids the explicit use of interval arithmetic. Instead, the different uncertain parameters are sequentially incorporated into the scheduling task through different stages and interval‐based formulation. The developed methodology incorporates weather, load, energy pricing and plug‐in electric vehicle related uncertainties. A benchmark case study in a smart prosumer layout serves to prove the effectiveness of the new approach. [ABSTRACT FROM AUTHOR]

This paper proposes an improved version of the Hunter‐prey optimization (HPO) method to enhance its search capabilities for solving the Optimal Power Flow (OPF) problem, which includes FACTS devices and wind power energy integration. The new algorithm is inspired by the behavior of predator and prey animals, such as lions, wolves, leopards, stags, and gazelles. The primary contribution of this study is to address the tendency of the original HPO approach to get trapped in local optima, by proposing an enhanced Hunter‐prey optimization (EHPO) approach that improves both the exploration and exploitation phases. This is achieved through a random mutation for exploration and an adaptive process for exploitation, which balances the transition between the two phases. The performance of the EHPO algorithm is compared with other optimization algorithms, and subsequently, it is used to solve the OPF problem incorporating FACTS devices and wind power. The results demonstrate the effectiveness and superiority of the proposed algorithm. In conclusion, this study successfully enhances the EHPO algorithm to provide better accuracy and faster convergence in finding optimal solutions for complex real‐world problems. [ABSTRACT FROM AUTHOR]

This paper presents the optimal scheduling of active distribution network (ADN) with linear and non‐linear loads in the presence of an integrated unit of electric spring and electric vehicle parking lot (IUEE). The proposed scheme is formulated in the form of three‐objective optimization, which minimizes the expected operating cost of the ADN, voltage deviation function (VDF), and the total harmonic distortion (THD) of the voltage in separate objective functions. Constraints include harmonic power flow (HPA) equations, operation and power quality limits, and operation model of the IUEE. This scheme has uncertainties of load, energy price, and energy demand of EVs, which are modelled here using stochastic programming. Next, the sum of the weighted functions method as a subset of the Pareto optimization technique is adopted to extract the integrated single‐objective problem. Then, Sine‐Cosine Combined Algorithm (SCA) and Gray Wolf Optimization (GWO) are used to achieve an optimal solution. In the end, by examining the numerical results obtained from the implementation of the proposed scheme on a 69‐bus ADN, the capability of the design in enhancing the economic situation, operation, and power quality of the ADN compared to network power flow studies obtained by optimal scheduling of the IUEE is confirmed. [ABSTRACT FROM AUTHOR]

Economic operation and reliable supply‐demand balance are problems of paramount importance in power grids with a massive share of intermittent renewable energy sources (RESs) of great interest. This paper sought an optimal coordinated generation scheduling for day‐ahead power system operation considering RESs and energy storage units. Renewable power generation, particularly, wind and photovoltaic are uncontrollable, whereas can be predicted using forecasting models. Within the proposed framework, a hyperparameter‐optimized long short‐term memory (LSTM) regression model is employed to forecast the day‐ahead weather from the historical time‐series weather data. Eventually, an empirical formula is used to estimate the power conversion from the day‐ahead weather forecasts for a selected PV module and wind turbine. The objective of the scheduling framework is to keep a delicate supply‐demand balance at the lowest possible cost of generation while maintaining the prevailing generation and system constraints. A variance measure uncertainty handling‐based grey wolf optimizer (GWO) technique is used to find the optimal day‐ahead generation schedules and dispatches under RESs forecast uncertainty. The proposed generation scheduling framework is examined on the IEEE 6 and 30‐bus systems. In the studied scenarios, the coordinated operation of generators can decrease the total day‐ahead operating cost for the modified IEEE 6‐bus system by 2.57% compared to supplying electricity generation with conventional generators alone. Likewise, the total operating cost from the coordinated operation of all generation portfolios was reduced by 6.93% from the operating cost of generation during base case simulation (supply only from dispatchable thermal units) on the modified IEEE 30‐bus system. Moreover, the case studies show that coordinated generation scheduling can mitigate the RESs power variability problem, provide secure supply‐demand operation, and minimize the operating cost of electricity generation. [ABSTRACT FROM AUTHOR]

A wide range of remedial action schemes (RAS) have been developed and deployed in field to take automated control actions to maintain system reliability and stability during adverse system conditions. According to the regulatory standards from North American Electricity Reliability Corporation (NERC), RAS applications deployed in field and their functionalities are required to be tested periodically. To do testing for the RAS, regular process that requires taking critical components offline is usually labor‐intensive and time‐consuming, especially for complex applications utilizing multiple measurement units and controllers from different substations. To overcome these challenges, this paper presents a remote testing architecture for remedial action schemes. Our proposed remote testing architecture is capable of accessing test signal generators, phasor measurement units (PMUs), RAS control units from multiple substations, managing tests for both centralized and distributed implementations of RAS. To address the effectiveness of the remote testing architecture, this paper presents the minimal wind curtailment applications with detailed alternative formulations of optimization models for RAS and highlights performance of the proposed remote testing of RAS. Simulation results for an IEEE standard test system and representative industry test cases demonstrate effective performance of the developed RAS testing mechanism. [ABSTRACT FROM AUTHOR]

Voltage/VAR regulation (VVR) implemented by capacitor banks (CBs), on‐load tap changers (OLTCs), and photovoltaic (PV)‐associated inverters is effective to enhance voltage stability and reduce power loss. On the other hand, conservation voltage reduction (CVR) has been widely adopted in distribution networks to reduce load demand. Existing works mainly focus on each of them. This paper proposes a VVR method with CVR, forming a multi‐objective optimisation problem which minimises (i) voltage collapse proximity indicator (VCPI), (ii) load demand, and (iii) power loss. Besides, PV power generation as uncertainty significantly impairs the VVR results. To deal with the uncertainty issue, this paper proposes a rolling‐horizon framework to determine VVR and applies Taguchi's orthogonal array testing (TOAT) to model the uncertain PV power generation, achieving rolling‐horizon‐based multi‐objective robust VVR. The proposed model is tested and demonstrated on the IEEE 33‐bus and 69‐bus systems, and simulation results verify that the proposed method can support robust solutions of the multi‐objective VVR problem for decision‐making. [ABSTRACT FROM AUTHOR]

Mid-long term voltage stability refers to voltage stability concerning the dynamics of the slow dynamic elements and protection devices in power systems. To improve the mid-long term voltage stability, this paper proposes a scheme of model predictive control (MPC) based on Galerkin method. The basic principle of MPC is to solve the optimization problem regarding voltage stability control based on the prediction model periodically. By applying the optimal control scheme to power systems, the mid-long term voltage stability can be enhanced. The Galerkin method-based polynomial approximation approximates the relationship between mid-long term trajectories and control parameters by a polynomial function. The polynomial function acts as a prediction model to predict future system trajectories under different values of control parameters in MPC. The advantage of the proposed method is that its prediction accuracy is relatively high and thus the control efficiency of MPC is improved, as the polynomial function can embody the non-linear characteristics of the power system. In the case study, the 3-machine 10-bus system and the New England 39-bus system are taken as examples to compare the MPC based on the proposed method and the trajectory sensitivity method in terms of prediction accuracy, computation time and control results. [ABSTRACT FROM AUTHOR]

Power transformers are vital to delivering energy in power grids and they bear considerable maintenance costs annually. One of the major issues for power transformers is the ageing of the insulation paper used in windings that affects the life of transformers significantly. Utilising an appropriate procedure for loading of transformers can reduce the ageing rate of insulation and can improve the life expectancy. Therefore, it is crucial to obtain the values for the ageing rate which are optimum and to prevent transformer fleet from accelerated ageing. Although techniques are available to calculate the ageing rate and loss of life of a single transformer, utilities looking into the transformer fleet ageing need a better tool to improve the ageing rate of all transformers together. The main contribution of this study is to provide a method for the transformer fleet ageing optimisation rather than single unit ageing approach. This study proposes a novel approach utilising different optimisation techniques that are capable of representing different factors in order to obtain an optimum ageing rate for a fleet of transformers. The optimum ageing rate can provide a technical tool for asset managers, utility engineers and owners for better power transformer fleet management. [ABSTRACT FROM AUTHOR]

Power transformers are vital to delivering energy in power grids and they bear considerable maintenance costs annually. One of the major issues for power transformers is the ageing of the insulation paper used in windings that affects the life of transformers significantly. Utilising an appropriate procedure for loading of transformers can reduce the ageing rate of insulation and can improve the life expectancy. Therefore, it is crucial to obtain the values for the ageing rate which are optimum and to prevent transformer fleet from accelerated ageing. Although techniques are available to calculate the ageing rate and loss of life of a single transformer, utilities looking into the transformer fleet ageing need a better tool to improve the ageing rate of all transformers together. The main contribution of this study is to provide a method for the transformer fleet ageing optimisation rather than single unit ageing approach. This study proposes a novel approach utilising different optimisation techniques that are capable of representing different factors in order to obtain an optimum ageing rate for a fleet of transformers. The optimum ageing rate can provide a technical tool for asset managers, utility engineers and owners for better power transformer fleet management. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach to designing technical and financial protocols needed to support the penetration of distributed energy resources (DERs). It first formulates a complex, hard‐to‐implement, centralized decision‐making objective for providing end‐to‐end electricity service. It then introduces a new taxonomy of an end‐to‐end interactive operations planning framework. The taxonomy rests on the dynamic monitoring and decisions systems (DyMoNDS) principles for supporting interactive protocols of (i) end‐to‐end interactions within a complex, multilayered multi‐voltage power system; (ii) dynamic energy resource management system (DERMS) interactions with their DERs as well as with the bulk power system (BPS) operators; and (iii) DERs interactions with DERMS. The distributed model predictive control for creating physically implementable cost functions is essential. Also, the minimal coordination of different layers utilizes an AC optimal power flow that is essential for ensuring power flow delivery. We next provide a proof‐of‐concept illustration on the IEEE 14 bus system augmented by two standardized microgrids of the proposed interactive protocols, and their potential use for enhancing dynamic host capacity (DHC). While novel, this approach is a natural outgrowth of the existing industry operations: It only requires enhancing decision‐making tools by the stakeholders, and carefully‐defined protocols for implementing their interactions. [ABSTRACT FROM AUTHOR]

Power system automation is an effective tool from both economical and technical aspects to improve the optimal operation of power generators. In this regard, Security Constrained Unit Commitment (SCUC) incorporating Dynamic Thermal Line Rating (DTLR) of overhead transmission lines can boost the system security effectively. Using Transmission Switching (TS) tool in SCUC problems leads to cost reduction. Still, one of the main challenges arisen in TS problems is the excessive number of switching in lines, which decreases the lifespan of power switches. In this paper, an Improved Linear AC Optimal Power Flow (ILACOPF) is proposed by using TS and considering Heat Balance Equation (HBE) as a security constraint. Merging dynamic thermal line rating (considering the weather conditions) in SCUC with TS, besides decreasing the number of switching and increasing the lifespan of power switches, causes a remarkable reduction in operating costs. In this power flow, system losses are modeled by linear formulations. Moreover, a linear approximation of the heat losses due to power flow through lines is proposed. To solve the proposed model, Benders' decomposition approach is applied. The performance of the proposed framework has been evaluated on 6‐bus and 118‐bus IEEE test systems. [ABSTRACT FROM AUTHOR]

This paper proposes a layered stochastic optimization approach for residential demand response (DR) under real‐time pricing (RTP) and an incentive‐based mechanism, which contains three steps. In the first layer, an independent system operator (ISO) announces day‐ahead RTP to a residential load aggregator (RLA). The RLA predicts individual household loads (step 1) and aggregates the loads to minimize electrical cost (step 2). In the second layer, the RLA announces incentives to homes, and home energy management systems (EMS) control the loads to maximize the reward in real‐time (step 3). In Step 1, probability based individual load prediction models are developed. In Step 2, a stochastic optimization model is developed to aggregate controllable loads of residential consumers. In Step 3, an incentive‐based mechanism is proposed, based on which, a real‐time load control model for individual homes is developed to benefit the RLA and homeowners. A highly efficient real‐time control algorithm for home EMS is developed. The case studies show that, with 10% controllable energy integration, the peak demand is reduced by 17.5% and the energy cost of the controllable loads is reduced by 28%. The proposed mechanism can effectively aggregate many individual residential controllable loads to participate in an electricity market. [ABSTRACT FROM AUTHOR]

This paper is intended to give an effective coordination method between high penetration of plug in electric vehicles (PEVs) and optimal operation management (OOM) of distribution network problem. An effective fuzzy logic controller (FLC) is proposed which focuses on coordinated smart charge/discharge control of high penetration of PEVs with OOM in an iterative optimization process. In each iteration, the proposed FLC receives the voltage of the connection node of the PEVs, the remaining time to the peak load and the available energy of the PEVs while appropriately specifies the amount of absorbed or injected active power of the PEVs. This coordination leads to the minimization of the electric network losses, voltage flattening and reducing the produced pollutant. A modified version of Sine‐Cosine Algorithm (MSCA) is used to minimize the objective functions while the problem constraints are satisfied. An appropriate 33‐bus distribution network with 1000 PEVs in 100 groups is considered in order to show the authenticity and efficiency of this work. The results show that the proposed FLC‐based coordination procedure could effectively adjust the exchange power of the PEVs with the OOM decision variables in an acceptable time duration. [ABSTRACT FROM AUTHOR]

With the increasing proportion of wind power connected to grid, power system dispatching is facing more and more challenges from uncertainty. To cope with this uncertainty, robust optimization has been applied in unit commitment (UC) problem. In this paper, a multi‐band uncertainty set considering the temporal correlation (MBUSCTC) of wind/load prediction error is proposed firstly, which has two characteristics: (1) The MBUSCTC rigorously and realistically reflect the distribution characteristics of uncertainties in uncertainty intervals, thereby effectively reducing the conservatism of the traditional singe‐band uncertainty set; (2) the temporal correlation constraints of wind power/load prediction errors in MBUSCTC could limit the realization of uncertainties fluctuating frequently in uncertain intervals, thereby eliminating scenarios with lower probability in uncertainty sets. Then the proposed MBUSCTC is applied to UC problem, leading a robust UC model based on MBUSCTC, which is solved by Benders decomposition method and C&CG method. Finally, case studies based on the modified IEEE‐118 bus system and an actual power system of China demonstrate that the proposed method can effectively reduce the conservativeness of the robust UC model and ensure the robustness of the unit commitment solution. [ABSTRACT FROM AUTHOR]

A horizontal time decomposition strategy to reduce the computation time of security‐constrained economic dispatch (SCED) is presented in this study. The proposed decomposition strategy is fundamentally novel and is developed in this paper for the first time. The considered scheduling horizon is decomposed into multiple smaller sub‐horizons. The concept of overlapping time intervals is introduced to model ramp constraints for the transition from one sub‐horizon to another sub‐horizon. A sub‐horizon includes several internal intervals and one or two overlapping time intervals that interconnect consecutive sub‐horizons. A local SCED is formulated for each sub‐horizon with respect to internal and overlapping intervals' variables/constraints. The overlapping intervals allow modelling intertemporal constraints between the consecutive sub‐horizons in a distributed fashion. To coordinate the subproblems and find the optimal solution for the whole operation horizon distributedly, accelerated auxiliary problem principle is developed. Furthermore, the authors present an initialisation strategy to enhance the convergence performance of the coordination strategy. The proposed algorithm is applied to three large systems, and promising results are obtained. [ABSTRACT FROM AUTHOR]

The grasshopper heuristic optimisation algorithm (GOA) is one of the newest heuristic techniques. It attempts to imitate locust's behaviour in solving different problems. In this paper, first, optimal location and size of fixed capacitor banks on distribution systems have been obtained using the GOA. Determination of the optimal number of capacitor banks and their optimal locations and sizes represent one of the major challenges facing distribution system operators. Therefore, second, a combined power loss sensitivity and GOA technique has been introduced to obtain the optimal number, location and size of the capacitor banks on distribution systems. Third, a proposed technique for optimal placement of fixed and switched capacitor banks considering daily load variations is introduced. Finally, a new technique of combined rough set theory and GOA is proposed to minimise daily switching of the capacitor banks and minimise daily power losses. Four test systems with different sizes and complexities are considered to evaluate the proposed techniques which are the 33, 69, 85 and 141‐bus systems. To clarify the validation and effectiveness of the proposed solution techniques, the obtained results have been compared with other previously used solving techniques. The obtained results demonstrate the accuracy and effectiveness of the introduced techniques. [ABSTRACT FROM AUTHOR]

Electric vehicle (EV) charging will bring new challenges to the coordination of grid and EV load. To facilitate large‐scale EV applications, optimal scheduling of EVs has become essential. However, the power grid has no power to force EV users to charge or give up charging. The actual type and mode of user's charging, may deviate from the expectations of grid. When the number of deviation's EV is sufficient, the power grid schedule will not achieve the expected results. In this context, a multi‐objective scheduling method considering user's charging selection is proposed to determine the optimal EV charging. The novel contributions of this paper lie in the exploitation of the user's preferences and their criterion of charging selection in form of functions based on concepts from economics. Three types of charging preferences for EV users including radical, conservative and balanced are used to influence the charging cost of EVs and load variance of grid. Finally, the ideal scheduling and actual scheduling are used to simulate various scenarios. The extensive results show that by introducing a method considering the influence of EV user's selection, the efficacy of the proposed scheduling can be further improved. [ABSTRACT FROM AUTHOR]

This paper presents the design of a non‐linear recurrent artificial neural network (ANN) based load frequency control (LFC) of a two‐area power system interconnected via HVDC tie‐line in parallel with EHVAC line. The control design is based on the combination of robust LFC and optimisation of recurrent ANN so that designed LFC scheme is suitable to handle the diverse operating conditions of power system. The various structures of the state cost weighting matrix (Q), i.e. controllability and observability aspects which affect the dynamics of the power system are used for the LFC design. The feedback gains achieved by the implementation of robust LFC using the different structures of Q are used to effectively train the recurrent ANN‐based LFC design and its performance is analysed with and without system non‐linearity's for 1% step load disturbance in one of the control areas to show the superiority of one design over the others. The closed‐loop system eigenvalues obtained for the system ensure the system stability. Furthermore, the recurrent ANN‐based LFC performance is evaluated for the diverse system operating conditions and compared with multi‐layer perceptron (MLP) ANN‐based and conventional PI control schemes to demonstrate the effectiveness of proposed LFC design scheme. [ABSTRACT FROM AUTHOR]

This paper proposes a Stackelberg game‐based optimal energy management model for a microgrid with commercial buildings (CBs), which include a cluster of flexible loads, such as a heating, ventilation, and air conditioning (HVAC) system and a lighting system. In particular, the microgrid operator (MGO) determines the optimal energy management scheme while the CBs enjoy a dynamic pricing tariff to adjust their consumption patterns for cost saving. The interactions between MGO and CBs are formulated as a bi‐level optimisation problem where the MGO behaves as a leader and CBs act as followers. The proposed model is transformed into a mixed integer linear programming (MILP) problem by jointly using the Karush–Kuhn–Tucker (KKT) condition and the strong duality theory. Besides, the effects of correlated solar irradiance and solar illuminance uncertainties on the load profile are taken into account through invoking the Nataf transformation‐based 2M + 1 point estimate method (PEM). Finally, case studies are served for demonstrating the feasibility and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Modern power systems consist of power electronics devices, which are used in renewable energy (RE) conversion. However, these devices, associated controllers, and uncertainty in RE output could bring new challenges to power system stability, especially oscillatory stability. Hence, the integration of battery energy storage systems (BESSs) is being developed to minimise the uncertainty and variability in renewables. Furthermore, to tackle the complex dynamics and inertia‐less characteristics of wind and PV plants additional controllers such as power oscillation damping (POD) control and virtual inertia scheme are sought. However, the primary challenges associated with the wide‐area oscillation damping controller are signal transmission delay, loss of communication signal, data drops, and others. This paper proposes a bat algorithm (BA) based resilient wide‐area multi‐mode controller (MMC) for enhancing oscillatory stability margin with high penetration of renewable power generations (RPGs) and BESSs. The Java 500 kV Indonesian grid is used to evaluate the performance of the resilient wide‐area MMC. From the results, it is found that the proposed controller effectively damp the critical mode of oscillation in the system even under communication failure as well as certain damping controller failures. [ABSTRACT FROM AUTHOR]

The objective of this paper is to suggest a suitable design procedure that may be useful for the practical realisation of the bi‐layer wide area control (WAC) architecture. The bi‐layer WAC architecture indicates the deployment a communication‐free supplementary WAC loop in parallel to the conventional WAC loop. The bi‐layer WAC system was found to have the potential to counterbalance the communication‐related problems that arise in the conventional WAC loop. However, until now, no specific work has been reported in literature for the practical design of the supplementary wide area damping controller (WADC). It is essential to design the supplementary WADC in a way so that it can be practically implemented without the support of any communication network. The particular problem is resolved here by defining a suitable structure of the feedback gain matrix in the supplementary WAC loop. A novel structurally constrained H2 norm optimisation technique is developed to design the particular feedback gain matrix with the proper structure. Here, the structurally constrained H2 norm optimisation is performed for an output feedback loop instead of a state feedback loop. The usefulness of the supplementary WADC design produced is thoroughly verified through real‐time (RT) experimentations. [ABSTRACT FROM AUTHOR]

Traditional expansion planning models reflect usage‐based practices and do not consider the complete utilisation of existing resources. This problem arises due to non‐inclusion of price‐based incentives provided to the consumers who incur additional network reinforcement or expansion cost. The paper proposes a generation and transmission expansion planning framework that includes price‐based incentives to define the future nodal load growth. The framework implements long‐run incremental cost (LRIC)‐based pricing signals for economically distributed demand to minimise consumer usage charges as well as network investment. Peak demand conditions by consumers are further minimised by the time‐of‐use‐based demand response mechanism. The combined effects of price‐based response and demand response are reflected in the future demand which serves as the basis for combined generation and transmission expansion planning. The case studies highlight the drawbacks of the conventional approaches which does not maximise the utilisation of existing grid resources while the proposed framework results in delayed investment strategies as a collective effort of the users and planner to optimise grid resources and the cost of assessing the electricity. [ABSTRACT FROM AUTHOR]

Incentive regulations of reliability have made a link between distribution companies' revenue and their service reliability. The companies have to decide how much to spend on various projects to provide an acceptable level of reliability while anticipation of load growth. Planners and decision makers require a comprehensive framework to optimally allocate available budgets to different plans with the highest benefits considering implementation of incentive regulation. This paper proposes a decision framework for the optimum share of expansion and reliability oriented plans in presence of reward–penalty mechanisms. A two‐layer optimization model is introduced, where in the outer layer, an iterative algorithm is applied to determine the optimal set of long‐term projects including Distributed Generations (DGs) installation. A heuristic optimization algorithm is employed in this layer. Considering long‐term plans, in inner optimization layer, the optimal set of mid‐term plans including feeder reinforcement, and preventive maintenance actions are determined using algorithms such as Branch‐and‐Cut and dynamic programming techniques. The model is further implemented on a test distribution network and the results are investigated through various case studies. Obtained results show the strong influence of incentive regulation on reliability indices. [ABSTRACT FROM AUTHOR]

Conventional power system was originally designed to provide efficient and reliable power. With the integration of information technology and advanced metering infrastructure, the power grid has become smart. The smart meters have allowed the system operators to continuously monitor the power system in real time and take necessary action to avoid system failures. Malicious actor, with access to the smart meters can modify sensor measurements to disrupt the operation of power system. To make the power system resilient to such cyber‐attacks, it is important to study all possible outcomes of cyber‐intrusions. In this paper, we present an attack on security constrained optimum power flow. We show with the help of case studies how an attacker, by injecting false data in load measurement sensors, can force system operator to change the dispatch and hence make the power system N –1 in‐compliant. The attack is modeled as a bi‐level optimization problem, aiming to find the minimum set of sensors required to launch the attack. From the system operator's perspective, critical lines and critical generators vulnerable to false data injection (FDI) attack are identified. IEEE 14 bus and 30 bus test systems are used to test the vulnerability of the power system against FDI attacks. [ABSTRACT FROM AUTHOR]

Energy poverty limits the economy and social development throughout the world. Wind turbine reduces the energy costs and facilitates the development of renewable energy industry, which provides an effective solution to energy crisis and environment pollution and develops rapidly in recently years. In this paper, a radial basis function neural network (RBFNN) optimisation model predictive control (MPC) was proposed for large wind turbines. In accordance with the complexity and uncertainty of wind turbine operation, a linear model based on the blade element momentum theory was established and the influencing factors of the proposed model were evaluated. The MPC taking into full account three degrees of freedom control multivariate was enforced by RBFNN prediction model, which meets the requirements of specified operation region. Additionally, the RBFNN prediction model with the memory of complicated rules and changed trend was trained by a great deal of historical data. The RBFNN in combination with MPC solves global optimisation problems and improves the dynamic performance of system. Simulation results for three‐bladed 5 MW onshore wind turbine verified the effectiveness of the proposed method and confirmed the fact that the fatigue loads were significantly reduced in the turbine tower. [ABSTRACT FROM AUTHOR]

This paper presents a new hybrid method for solving the proposed multi‐level optimisation problem, as described in Part I. The hybrid method employs a novel two‐stage multi‐dimensional melody search algorithm (MSA) with an innovative alternative improvisation procedure (AIP). This hybrid method handles the different levels of the proposed multi‐level framework. The two‐stage multi‐dimensional MSA is a recently developed optimisation algorithm, which is derived from the harmony search algorithm (HSA). Unlike the HSA, the MSA uses different player memories and a group improvisation procedure for finding the best substitution of pitches within a melody. These two features can enhance the performance of the MSA in comparison with the HSA. In addition, to improve the efficiency of the proposed MSA, an innovative improvisation scheme based on the AIP is used for generating new melodies. The proposed methodology has been applied on the 46‐bus south Brazilian electric power grid and the IEEE 118‐bus test system under two different scenarios in order to demonstrate the feasibility and capabilities of the newly developed framework. The obtained results from case studies validate sufficiency and profitableness of the offered multi‐level framework. [ABSTRACT FROM AUTHOR]

One of the main uncertainties in long‐term distribution network planning is load forecasting error. This paper presents a probabilistic multistage expansion planning method to consider the load forecasting error as well as pseudo dynamic behavior of network parameters and geographical constraints. The optimal routes of MV feeders as backbone of distribution networks are obtained for both mid and long‐term case with deterministic and probabilistic modeling. The Imperialist Competitive Algorithm (ICA) is adapted for the optimal expansion planning of MV distribution network. To check and maintain the radial configuration of the MV distribution network during ICA random operators, a novel algorithm is employed. Comparative tabular and graphical results are given for deterministic and probabilistic planning. In probabilistic case the results is also represented by histogram of statistics data, mean and standard deviation of input and output variables using Monte Carlo Simulation (MCS). Based on the results it is possible to design the MV network at the presence of uncertainties more robust and flexible with respect to deterministic planning without reasonable increase in total cost. The efficiency and capability of the method is tested on a relatively large‐scale distribution network. [ABSTRACT FROM AUTHOR]