Power Dispatch Problem Research Articles

A novel solution for the optimal reactive power dispatch problem using an artificial neural network integrated with the firefly optimization algorithm

This article applies a novel intelligence technique to solve power system issues faced daily. Compensation for reactive power is a significant issue faced by power system operators in research. The solution can be obtained by handling a multi-objective task and multiconstraints by reducing the active power loss and minimizing the voltage deviation at the load end. The novelty of the research focuses on integrating artificial neural network techniques with the firefly algorithm, a novel optimization algorithm for attaining an objective function. The Levenberg–Marquardt back-propagation algorithm is most suited for proper tuning of the control variables. The objective of this research can be attained by appropriately tuning the control variables connected with the IEEE test bus systems, which helps to maximally improve the voltage profile. Existing research studies have focused on reactive power management, which is attained by solving optimal reactive power flow problems employing nature-inspired approach techniques such as the symbiotic organism search algorithm, the cuckoo search algorithm, the black hole algorithm, the krill herd algorithm, and whale optimization. The evolving strategy, the firefly algorithm (FFA), minimizes the multiconstraint functions more competently and effectively than any conventional algorithm. To showcase the strength of the firefly algorithm incorporating AI, it is examined on standard IEEE test bus systems, namely, the 14-, 30-, and 58-bus networks. The obtained results quantify the effectiveness of the proposed methodology, that is, the artificial intelligence technique implementing the firefly algorithm gives better results than conventional methods.

Scenario-Based Approach to Solve Optimal Reactive Power Dispatch Problem with Integration of Solar Energy Using Modified Ant Line Optimizer

Scenario-Based Approach to Solve Optimal Reactive Power Dispatch Problem with Integration of Solar Energy Using Modified Ant Line Optimizer

Hybrid particle swarm and sea horse optimization algorithm-based optimal reactive power dispatch of power systems comprising electric vehicles

The reliability and effectiveness of today's electrical grids rely heavily on optimal reactive power dispatch (ORPD). The ORPD problem gets more challenging to resolve in the setting of ever-increasingly dynamic and complex systems. To handle the ORPD while also considering the existence of electric vehicles, this research introduces a novel technique: the Hybrid Particle Swarm Optimization and Sea Horse Optimization (PSOSHO) algorithm. Byreducing both active power loss and voltage variation, the proposed PSOSHO approach aims at improving the efficiency and reliability of the power grid. Simulation studies on reference power grids, including the IEEE 30-bus and IEEE 57-bus networks, verified its efficacy. Existing metaheuristic optimization techniques are compared using the same restrictions, governing parameters, and data. The results show that the PSOSHO method is trustworthy and effective in resolving the ORPD problem. One of the most pressing issues facing today's electricity grids is how to accommodate the growing number of electric vehicles. Real data on electric vehicles are incorporated in the analyses to obtain a realistic study. The suggested PSOSHO algorithm is a significant step forward in this area, providing a reliable and effective answer to the problem of optimum reactive power dispatch and helping to ensure the long-term viability of power systems in the age of electromobility.

An enhanced arithmetic optimization algorithm for optimal control of reactive power

AbstractIn this article, an enhanced arithmetic optimization algorithm (EAOA) is utilized to resolve the optimal reactive power dispatch problem (ORPD) of power plants, which is a non‐linear, non‐smooth, complex optimization problem. Typically, it is formulated as a constrained optimal power flow problem. This paper utilizes the gamma distribution to generate initial random solutions, which improves the accuracy of the arithmetic optimization algorithm (AOA). The original mode of deviation of math optimizer accelerated (MOA) is replaced with the improved pattern of change in the prey's energy in Harris Hawk Optimization (HHO), which creates a superior balance between the exploration and exploitation of AOA. This paper implements logarithmic and exponential operators in place of division and multiplication operators, which improves the exploration ability of AOA and also utilizes the gamma distribution in the generation of random numbers, which are essential for the selection of operators in both search phase which enlarges the search area and improves the global optimum solution. A parametric analysis is performed on 10 benchmark functions for nine possible values before selecting the algorithm's initial parameters. A comparative statistical analysis along with Friedman's test is carried out on 23 benchmark functions to test the overall performance as compared to Sine‐Cosine Algorithm (SCA), Swarm‐Salp Algorithm, Grey Wolf Optimization (GWO), and basic Arithmetic Optimization Algorithm (AOA). EAOA is applied to the IEEE 57 bus system for optimal reactive power control. EAOA reports a significant reduction in a power loss of 23.2 MW, which is 18.596% smaller than the system's base case power loss of 28.5 MW as compared to Artificial Bee Colony (ABC), Firefly Algorithm (FA), Bat‐inspired Algorithm (BAT), Cuckoo Search Algorithm (CSA), and Modified Ant Lion Algorithm (MALO) which reports a reduction in a power loss of 14.73%, 15.43%, 15.43%, 16.14%, and 16.14% respectively. EAOA reduces the TVD to 0.76 pu, compared to ABC, FA, BAT, CSA, and MALO, which minimizes the TVD to 0.84, 0.82, 0.88, 0.82, and 0.79, respectively. This proves that EAOA performs better in resolving ORCP problems regarding the quality of solutions and convergence. A detailed comparative statistical analysis of EAOA with CSA, BAT, and WOA (Whale Optimization Algorithm) is conducted in terms of Probability Distribution Functions (PDF) and Cumulative Distribution Functions (CDF), along with the Wilcoxson sign rank test, which proves that EAOA gives the solution with a higher degree of precision.

Optimal Reactive Power Dispatch problem using exchange market based Butterfly Optimization Algorithm

The Butterfly Optimization Algorithm (BOA) is a swarm-based optimization technique which takes its inspiration from the butterflies’ foraging activity. BOA has gained extensive popularity among the research community and is now utilized to tackle a variety of optimization challenges. However, literatures suggest that its exploration and exploitation are not properly balanced. To overcome this problem, BOA is fused with the crossover of Exchange Market Algorithm (EMA) and with non-uniform mutation. In this paper, enhanced BOA (EBOA) is used for solving the Optimal Reactive Power Dispatch (ORPD) problems. ORPD is traditionally a power system optimization tool that regulates control variables such as Generator bus Voltage, tap settings of tap-changing transformers, and VAR output of compensating devices in order to reduce real power loss, improve voltage deviation, and increase voltage stability. The suggested technique is evaluated using the IEEE 30 bus standard test system, IEEE 118 bus standard test system and Indian 62 bus system. To determine the efficacy of the algorithm, the findings from BOA and EBOA are compared to those produced by other researchers and published in the literature. From the results it can be observed that the proposed algorithm is able to reduce the active power loss by 22%, voltage deviation by 91.6% and L-Index by 42.02 % from their corresponding initial value for IEEE 30 bus system. Similar results can be seen from other test system also. The proposed algorithm is also tested in solving benchmark problems. The simulated results confirm the efficiency and robustness of the algorithm for solving ORPD problems.

A multi-area intra-day dispatch strategy for power systems under high share of renewable energy with power support capacity assessment

Long-distance power support through High-voltage Direct Current (HVDC) has provided feasible solutions for power dispatch and control problems in multi-area power systems under high share of renewable energy. In this paper, an advanced multi-area intra-day dispatch strategy for power systems with high penetration of renewable energy considering power support capacity is established to increase the accommodation of wind power and enhance the stability of the power system. At first, a novel assessment method of regional active power support capacity is carried out in this paper, which can sufficiently quantify the regional power margin (RPM), the DC dispatchable power (DCDP), and the regional power support distance (RPSD) of a power system. Subsequently, a multi-area intra-day dispatch strategy based on power support capacity analysis is proposed, where constraints on conventional units, wind farms, transmission lines, and RPSD are developed. Finally, to enable the development of the dispatch model, a model predictive control (MPC) scheme is used to update the power support capacity indicators dynamically and improve model reliability. The effectiveness and superiority of the proposed strategy are verified by case studies. Numerical results show a 7.02% cost reduction and overload risk reduction by using the proposed strategy compared to conventional global optimization methods.

Incorporating photovoltaic inverter capability into stochastic optimal reactive power dispatch through an enhanced artificial gorilla troops optimizer

AbstractThis study addresses the challenging task of solving optimal reactive power dispatch (ORPD) while incorporating renewable energy resources (RERs), considering their stochastic and time‐varying nature. Specifically, the focus is on solving the stochastic optimal reactive power dispatch (SORPD) problem, taking into account uncertainties in load demand and generated power, as well as the reactive power generation capability of photovoltaic (PV) systems. To tackle this problem, an enhanced Artificial Gorilla Troops Optimizer (EGTO) is proposed, which utilizes multiple strategies. The objective is to minimize power loss and improve voltage profile and system stability under uncertain conditions. The algorithm is applied and tested on the IEEE 30‐bus system, both with and without the STATCOM functionality of the PV system. A comparison is conducted against other well‐known optimization algorithms. The results demonstrate the significant improvement achieved by incorporating the PV unit. The inclusion of the PV system leads to reduced expected power losses, voltage deviations, and improved voltage stability. Specifically, without and with the STATCOM functionality, the expected power loss decreases from 5.9218 MW at the base case to 1.1419 MW and 1.1197 MW, respectively. Similarly, the expected voltage deviation decreases from 1.9320 p.u. to 0.0909 p.u. and 0.0893 p.u., respectively, and the expected voltage stability decreases from 0.1336 p.u. to 0.07199 p.u. and 0.07142 p.u., respectively.

Multi-Agent Distributed Reinforcement Learning Algorithm for Free-Model Economic-Environmental Power and CHP Dispatch Problems

In conventional methods for Economic Dispatch Problem (EDP) and Economic-Environmental Dispatch Problem (EEDP), a full connection between the units and the control center is considered, while some faults in the communication system are possible in practical conditions. Hence, an intelligent and high-speed distributed method that is robust against disconnection is needed. In this paper, a Multi-Agent Distributed Reinforcement Learning (MADRL) algorithm based on consensus control for EDP and EEDP is presented. In this method, the incremental cost of units is optimized based on Lagrange method and the reinforcement learning algorithm. Thus, a performance index is defined for each agent in proposed algorithm to be independent of the unit model. The performance index of each agent is the sum of two terms, including i) the difference between the previous performance index value and local power and heat mismatch values and ii) the sum of the difference between the incremental cost of the unit with other neighboring units. In other words, optimizing the defined performance indexes of the agents eliminates the need for the system model. The MADRL method is tested on several grids and compared with other methods. The numerical results show an improvement in the algorithm speed and optimal point.

A State-of-the-Art Review on Optimization Methods and Techniques for Economic Load Dispatch with Photovoltaic Systems: Progress, Challenges, and Recommendations

Fossil fuel is considered to be the primary power generation source. As this source is not that eco- and environmentally friendly, researchers are constantly searching for an alternative source for power generation. Renewable energy has drawn much attention in this regard in recent times. For solving economic load dispatch issues, numerous operational constraints must be considered. Due to the restructuring of the power sector, there is competition between different power systems organizations. Increasing fossil fuel costs drive power-producing utilities to adopt a cost-effective technique for dispatching actual power output. Due to the presence of nonlinearity and non-convexity in the fuel of cost function of generators, the economic load dispatch is often considered a complex optimization problem. Many researchers have been optimizing fuel costs to solve the economic power dispatch problem. This paper offers a critical analysis of ELD that takes into account both traditional and non-traditional energy sources. The review covers a variety of algorithms, including hybrid algorithms for integrating renewable energy sources (RES). The paper also focuses on several restricted optimization techniques and contemporary algorithms including PSO, Jaya, GWO, SMO, TLBO, Rao, MRao-2, and MFO to reduce the fuel cost of generation units using large-scale solar PV. Moreover, this paper provides a comprehensive overview of the current state of economic load dispatch and provides valuable insights for electricity researchers and practitioners. It also discusses future technologies and next steps in the field of ELD, emphasizing the need for more environmentally friendly and cost-effective power generation and distribution solutions. Overall, the paper demonstrates the benefits of renewable energy sources as well as optimization techniques for creating a more sustainable and efficient power generation system.

Optimal Solution of reactive power dispatch under normal and contingency condition using Rao algorithms

Rao suggested three straight forward, metaphor-free optimization strategies to address the challenging optimization issue. These algorithms are approaches for optimization without parameters. As a result, no parameter modification unique to an algorithm is needed. This feature makes these algorithms simple to use and effective at resolving a wide range of difficult engineering and scientific challenges. This paper’s main objective is to use the Rao method to resolve the reactive power dispatch problem under both regular and emergency circumstances. The suggested method’s reliability was tested using these techniques in standard IEEE 30 bus test systems. Overall reactive power dispatch (RPD) results of Rao algorithms were matched with those of recently published well-known methods. Rao algorithms produce accurate and competitively priced numerical results.

Fuel constrained combined heat and power dispatch problem using marine predators algorithm

Fuel constrained combined heat and power dispatch problem using marine predators algorithm

Isolated UIPC-intertied AC-DC microgrids operation with novel dynamic flexible loads model

This work discusses the islanding operation of hybrid microgrids (HµGs) interconnected by a unified interphase power controller (UIPC). It is illustrated that considering islanding operation of HµG, the problem of power dispatch among intertied AC and DC µGs, as well as voltage and frequency control, can be handled by UIPC. The power dispatch among various AC and DC µGs using UIPC is analyzed and formulated through defining a simple demand condition function (DCF). A new control structure is proposed for the series power converter of the UIPC which enjoys the robustness of sliding mode control (SMC) concept and adopts a new energy management system (EMS) with embedded decision-making. This provides a fast, robust, and simple power flow control unit which acts as a general supervisory system for the UIPC and islanding operation of HµG. Also, a novel generalized dynamic model for flexible loads has been proposed. These loads have been directly controlled through UIPC supervisory system here. The analytical response of the proposed flexible load model is discussed and further developed using the nonlinear autoregressive-moving-average artificial neural network (ANN) model. The Levenberg-Marquardt learning algorithm is used to train the network based on the model estimation error. Besides, a new model predictive-based optimal control scheme is proposed for the flexible load. Simulation results using MATLAB support the usefulness of the proposed UIPC-based islanded HµG platform.

Managing Considerable Distributed Resources for Demand Response: A Resource Selection Strategy Based on Contextual Bandit

The widespread adoption of distributed energy resources (DERs) leads to resource redundancy in grid operation and increases computation complexity, which underscores the need for effective resource management strategies. In this paper, we present a novel resource management approach that decouples the resource selection and power dispatch tasks. The resource selection task determines the subset of resources designated to participate in the demand response service, while the power dispatch task determines the power output of the selected candidates. A solution strategy based on contextual bandit with DQN structure is then proposed. Concretely, an agent determines the resource selection action, while the power dispatch task is solved in the environment. The negative value of the operational cost is used as feedback to the agent, which links the two tasks in a closed-loop manner. Moreover, to cope with the uncertainty in the power dispatch problem, distributionally robust optimization (DRO) is applied for the reserve settlement to satisfy the reliability requirement against this uncertainty. Numerical studies demonstrate that the DQN-based contextual bandit approach can achieve a profit enhancement ranging from 0.35% to 46.46% compared to the contextual bandit with policy gradient approach under different resource selection quantities.

Optimal Solution of Environmental Economic Dispatch Problems Using QPGPSO-ω

The renewable energy sources (RESs)-based economic dispatch problem (EDP) is of vital importance for modern power systems. Environmental pollution, climatic degradation, and rapidly growing prices of continuously depleting fossil fuels have encouraged researchers to consider mechanisms for RES implementation and optimal operations. This paper presents a quasi-oppositional population-based global particle swarm optimizer with inertial weights (QPGPSO-ω) to solve environment friendly EDPs. The optimization technique is applied to solve the EDP under different scenarios including cases where only renewable energy sources (RESs) are used and the cases where combined emission–economic dispatch (CEED) problem is taken into account. The scenario for RESs includes a combination of six wind, five solar PV, and four biofuel systems for power generation. EDPs are considered without any constraints, and the variability of resources is depicted over time, along with the regional load-sharing dispatch (RLSD). The case of CEED considers ten thermal units with the valve point loading (VPL) effect and transmission losses. The results obtained by the proposed QPGPSO-ω algorithm are better than the reported results employing other optimization methods. This is shown by the lower costs achieved up to USD 8026.1439 for the case of only RES-based EDPs, USD 1346.8 for the case of RES-based EDPs with RLSD, and USD 111,533.59 for the case of CEED. Thus, the proposed QPGPSO-ω algorithm was effective in solving the various adopted power dispatch problems in power system.

A Modified Strength Pareto Evolutionary Algorithm 2 based Environmental /Economic Power Dispatch

A Strength Pareto Evolutionary Algorithm 2 (SPEA 2) approach for solving the multi-objective Environmental / Economic Power Dispatch (EEPD) problem is presented in this paper. In the past fuel cost consumption minimization was the aim (a single objective function) of economic power dispatch problem. Since the clean air act amendments have been applied to reduce SO2 and NOX emissions from power plants, the utilities change their strategies in order to reduce pollution and atmospheric emission as well, adding emission minimization as other objective function made economic power dispatch (EPD) a multi-objective problem having conflicting objectives. SPEA2 is the improved version of SPEA with better fitness assignment, density estimation, and modified archive truncation. In addition fuzzy set theory is employed to extract the best compromise solution. Several optimization run of the proposed method are carried out on 3-units system and 6-units standard IEEE 30-bus test system. The results demonstrate the capabilities of the proposed method to generate well-distributed Pareto-optimal non-dominated feasible solutions in single run. The comparison with other multi-objective methods demonstrates the superiority of the proposed method.

Using the Whale Optimization Algorithm to Solve the Optimal Reactive Power Dispatch Problem

The optimal reactive power dispatch (ORPD) is a complex, optimal non-meritorious control issue with continuous and discontinuous control variables. This article exhibits a whale optimization algorithm (WOA) motivated by the whale’s bubble-net hunting tactic to resolve ORPD. The intention is to comply with certain constraints to promote the voltage transmission quality by adequately altering the parameters. The WOA not only equalizes exploitation and exploration to maximize the overall performance and eliminate search stagnation but also has remarkable sustainability and robustness to accomplish superior convergence speed and computation accuracy. The WOA is contrasted with MFO, BA, GOA, GWO, MDWA, SMA, SPBO and SSA by diminishing the fitness value to highlight the superiority and stability. The experimental results reveal that WOA exhibits a superior convergence level and computation precision to accomplish the minimum active power loss and superior control variables.

A Chaotic Krill Herd Technique for Solving Combined Economic Emission Dispatch

The economic power dispatch (EPD) problem aims to find the optimal operational settings of power generating units with minimizing the entire production costs of generation and all system constraints are kept into their acceptable limitations. Meanwhile, achieving this economical operation based on the reduction of the fuel cost and fossil fuel fired power plant emission should be as little as possible. Another nonlinear framework, with contradictory objectives, called combined economical emission power dispatch (CEEPD) is considered as the target framework in this study. The CEEPD framework is subjected to a set of operational equality and inequality constraints. In terms of the solution methodology, this paper is focused at developing a novel optimization algorithm called Chaotic Krill Herd Algorithm (CKHA) as an efficient tool for solving non-convex EPD and CEEPD frameworks. The proposed CKHA is tested on several IEEE test systems for refining its convergence and therefore aggregating the overall solution method. The numerical results clearly show the dominance of CKHA in finding the competitive best fitness functions compared with the advanced methods those published in the literature.

Research on the environment & economic indicators for electricity trading

In this paper, the environmental and economic indicators based on the reactive power pricing are proposed so as to formulate the novel environmental economic power dispatch (NEED) problems for electricity trading. Here, a new way of calculating total cost is proposed based on the reactive power pricing instead of the pure fuel cost, which leads to the NEED problems. In addition, an improved tunicate swarm algorithm (ITSA) is proposed to deal with the NEED problems. Different from the original tunicate swarm algorithm (TSA), adaptive weight parameter and mutation process are used to improve the disadvantages of TSA which converges too fast and easily falls into local optimum. In combination with the Pareto dominance rule, the ITSA can be more effective in dealing with the NEED problems. In order to examine the performance of the ITSA, simulation tests of ITSA, TSA, and particle swarm optimization (PSO) are conducted in IEEE30 node system. During the experimental process, optimized objective functions include total cost considering reactive cost, pollution emission and power loss. The final comparison results show that the ITSA is able to get better distributed Pareto front (PF) and superior best compromise solution (BCS), which proves the high efficiency of the ITSA in dealing with the NEED problems.

Coordinated real-time power dispatch for integrated transmission and distribution networks based on model-based optimization and deep reinforcement learning

As massive distributed energy resources are connected to the distribution networks, the distribution networks are gradually transforming into active distribution networks, and the transmission and distribution networks become more coupled. To make the most of these distributed energy resources and improve the safety of the coupled power system, coordinated real-time power dispatch is indispensable. And for privacy protection and actual engineering needs, the real-time power dispatch problem should be optimized in a decentralized manner by different control centers. In this paper, we decompose the global objective function into local ones, which can be locally optimized by control centers of transmission and distribution networks. The coordination is then realized by introducing the alternating direction method of multipliers (ADMM). Based on the decomposition, we design a novel communication architecture, in which only the boundary variables are exchanged, effectively protecting private information. Additionally, in the real power system, the transmission networks have credible models, while the maintenance of accurate models for distribution networks is usually unaffordable. Therefore, this paper also proposes a hybrid real-time power dispatch method: a model-based optimization method for the transmission network and model-free deep reinforcement learning for each subordinate distribution network. This hybrid method not only overcomes the model incompleteness of the distribution networks but also accelerates and stabilizes the learning process. Numerical test results on three different cases justify the effectiveness of the proposed communication architecture and hybrid real-time power dispatch method.

Hybrid intelligence strategy for techno-economic reactive power dispatch approach to ensure system security

This work consolidates chaotic maps into a contemporary heuristic approach called Chimp Optimization Algorithm (COA). Augmentation of ten chaotic maps to COA forms the hybrid Chaotic COA (CCOA) which generates solutions in search space with enhanced mobility. A bi-level strategy of validating the robustness of the proposed hybrid method is adopted by first benchmarking it on eighteen assorted benchmark test functions and later by performing statistical analysis. The result obtained candidly establishes that merging of chaotic maps (dominantly Chaotic 4 Iterative type-CCOA4) enhances the performance of COA. The inferences congregated from the benchmark functions and statistical analysis facilitates the application of the recommended algorithm to obtain techno-economic solution in reactive power dispatch (RPD) problem which is manifested as a challenging issue in power system operation. The RPD problem is analyzed implementing the proposed approach with series-shunt compensation in a competitive electricity market for secured operation and the proposed Iterative type-CCOA4 reduces the total operating cost by 3.3450 × 103$ with respect to initial operating cost.