Power Dispatch Research Articles

Review of Detection Methods for Abnormal Electricity Consumption Data in Smart Grid

The smart grid is an intelligent system of the power grid, which is a communication information support platform based on the coordinated development of the transmission network and various levels of power grids. It is a highly integrated system characterized by informationization, automation, and interactivity of various voltage levels, including transmission and transformation, distribution, and power dispatch. This article summarizes, analyzes, and summarizes the methods for detecting abnormal electricity consumption data in smart grids. It introduces the detection methods for abnormal electricity consumption data based on traditional technology and artificial intelligence technology, analyzes and elaborates on the basic principles and characteristics of each method, summarizes and looks forward to the challenges and future development trends faced by abnormal electricity consumption data detection in smart grids, and provides some reference for subsequent research.

Solution of optimal reactive power dispatch by Lévy-flight phasor particle swarm optimization

Optimal reactive power dispatch (ORPD) problems are important tools for the sake of security and economics of power systems. The ORPD problems are nonlinear optimization problems to minimize the real power losses and voltage profile enhancement by optimizing several discrete and continuous control variables. This paper proposes a Lévy-flight phasor particle swarm optimization (LPPSO) for solving ORPD problems while considering real power losses and voltage profile in two standard power systems. The simulation results demonstrate that the LPPSO algorithm proves itself as an acceptable method for reaching a more optimal solution for the ORPD problems.

Dynamic verification of an optimisation algorithm for power dispatch of integrated energy systems

The urgent need to achieve net-zero carbon emissions by 2050 has led to a growing focus on innovative approaches to producing, storing, and consuming energy. Integrated energy systems (IES) have emerged as a promising solution, capitalising on synergies between energy networks and enhancing efficiency. Such a holistic approach enables the integration of renewable energy sources and flexibility provision from one energy network to another, reducing emissions while facilitating strategies for operational optimisation of energy systems. However, emphasis has been mostly made on steady-state methodologies, with a dynamic verification of the optimal solutions not given sufficient attention. To contribute towards bridging this research gap, a methodology to verify the outcomes of an optimisation algorithm is presented in this paper. The methodology has been applied to assess the operation of a civic building in the UK dedicated to health services. This has been done making use of real energy demand data. Optimisation is aimed at improving power dispatch of the energy system by minimising operational costs and carbon emissions. To quantify potential discrepancies in power flows and operational costs obtained from the optimisation, a dynamic model of the IES that better captures real-world system operation is employed. By incorporating slow transients of thermal systems, control loops, and non-linearity of components in the dynamic model, often overlooked in traditional optimisation modules, the methodology provides a more accurate assessment of energy consumption and operational costs. The effectiveness of the methodology is assessed through model-in-the-loop co-simulations between MATLAB/Simulink and Apros alongside a series of scenarios. Results indicate significant discrepancies in power flows and operational costs between the optimisation and the dynamic model. These findings illustrate potential limitations of conventional operational optimisation modules in addressing real-world complexities, emphasising the significance of dynamic verification methods for informed energy management and decision-planning.

Optimal economic environmental power dispatch by using artificial bee colony algorithm

<span>Today, most power plants worldwide use fossil fuels such as natural gas, coal, and oil as the primary resource for energy reproduction primarily. The new term for economic environmental power dispatch (EEPD) problems is on the minimum total cost of the generator and fossil fuel emissions to address atmosphere pollution. Thus, the significant objective functions are identified to minimize the cost of generation, most minor emission pollutants, and lowest system losses individually. As an alternative, an Artificial Bee Colony (ABC) swarming algorithm is applied to solve the EEPD problem separately in the power systems on both standard IEEE 26 bus system and IEEE 57 bus system using a MATLAB programming environment. The performance of the introduced algorithm is measured based on simple mathematical analysis such as a simple deviation and its percentage from the obtained results. From the mathematical measurement, the ABC algorithm showed an improvement on each identified single objective function as compared with the gradient approach of using the Newton Raphson method in a short computational time.</span>

Optimal power dispatch in hybrid power system for medium- and large-scale practical power systems using self-adaptive bonobo optimizer algorithm

Incorporating renewable energy sources (RESs) introduces a notable amount of uncertainty in the optimal planning and operation of electrical power grids. Under these circumstances, this paper proposes the application of a recently introduced metaheuristic optimization technique to solve the stochastic optimal power flow (OPF) problem involving wind and solar power sources. The self-adaptive bonobo optimizer (SaBO) is used to minimize three distinct objective functions: (i) Total generation cost (TGC) minimization, including both thermal and wind/solar generation costs, (ii) Power loss minimization, (iii) Combined generation cost and emissions effect minimization. The costs associated with the stochastic generation of wind and solar power included direct costs, reserves and penalty costs from the overestimation and underestimation of available wind and solar power, respectively. The performance of the proposed algorithm is evaluated on two power systems: the modified IEEE 30-bus and the Algerian DZA 114-bus test systems. To demonstrate the efficacy of the SaBO, the obtained results have been compared with those obtained from the Kepler optimization algorithm (KOA) and other recently published optimizers under the same case studies and constraints. The comparative results clearly show the superiority of the SaBO algorithm over all other well-known optimization algorithms provided in the literature for solving the OPF problem. This is evidenced by minimizing total generation costs of 781.2363 $/h for the modified IEEE 30-bus and 16,706.1630 $/h for the Algerian DZA-114-bus system. Furthermore, the integration of RES led to a notable 2.33% and 11.67% reduction in total generation cost for the IEEE 30-bus and Algerian DZA 114-bus systems, respectively, compared to their initial configurations without RESs. The promising findings highlight the powerful of the optimizer to solve non-linear and complex optimization problems in power systems.

Impact of demand growth on the capacity of long-duration energy storage under deep decarbonization

Abstract The weather-dependent uncertainty of wind and solar power generation presents a challenge to the balancing of power generation and demand in highly renewable electricity systems. Battery energy storage can provide flexibility to firm up the variability of renewables and to respond to the increased load demand under decarbonization scenarios. This paper explores how the battery energy storage capacity requirement for compressed-air energy storage (CAES) will grow as the load demand increases. Here we used an idealized lowest-cost optimization model to study the response of highly renewable electricity systems to the increasing load demand of California under deep decarbonization. Results show that providing bulk CAES to the zero-emission power system offers substantial benefits, but it cannot fully compensate for the 100% variability of highly renewable power systems. The capacity requirement of CAES increases by ≤33.3% with a 1.5 times increase in the load demand and by ≤50% with a two-times increase in the load demand. In this analysis, a zero-emission electricity system operating at current costs becomes more cost-effective when there is firm power generation. The least competitive nuclear option plays this role and reduces system costs by 16.4%, curtails the annual main node by 36.8%, and decreases the CAES capacity requirements by ≤80.7% in the case of a double-load demand. While CAES has potential in addressing renewable variability, its widespread deployment is constrained by geographical, societal, and economic factors. Therefore, if California is aiming for an energy system that is reliant on wind and solar power, then an additional dispatchable power source other than CAES or similar load flexibility is necessary. To fully harness the benefits of bulk CAES, the development and implementation of cost-effective approaches are crucial in significantly reducing system costs.

Co-optimization of power and spinning reserve in multi-area electrical networks with wind farms

Integrating wind farms into electrical networks reconfigures energy matrices and poses new challenges to system operators. For instance, wind farm owners must now guarantee spinning reserves stipulated in grid codes. This prompts an urgent quest for optimizing power and reserve allocation in liberalized markets using advanced models. In this sense, this paper introduces an optimal power flow (OPF) model to co-optimize power and spinning reserve in multi-area electrical networks with wind farms. The granularity and layout of the wind plant are considered for a correct calculation of the spinning reserve in each variable speed wind turbine. Unlike classical OPF studies, where generator participation is free and mandatory in system reserves, this new approach considers both power and reserve as variable factors in the optimization process, which depend on reserve costs, system load, and wind speed. The approach is evaluated through comparative analyzes on two power networks: a small 12-bus, two-area network with two wind farms and a larger 100-bus, four-area network with four wind farms. Multi-period 12-hour look-ahead analyzes are conducted with variable conditions of demand and wind speed. The studies demonstrate the advantages of the new model compared to the classical OPF, showcasing its ability to optimize power dispatch with wind farms actively contributing to the spinning reserve requirements of the power network.

Error prevention adaptive optimization model for power dispatch under time redundancy control

Error prevention adaptive optimization model for power dispatch under time redundancy control

Multi-time scale economic regulation model of virtual power plant considering multiple uncertainties of source, load and storag

A novel multi-stage time scale economic dispatch scheme is proposed for virtual power plants, taking into account the uncertainties arising from the connection of distribution network sources. This research introduces specific scheduling schemes tailored to various time scales within distribution networks, including a fuzzy optimized day ahead scheduling scheme, an intra-day scheduling scheme combined with Deep Q Network, and an adaptive optimized real-time scheduling scheme. This plan mainly considers the impact of photovoltaic output and conducts scheduling one day in advance through fuzzy optimization. In the intraday scheduling, different strategies were adopted in the study. By combining with Deep Q Network, research on scheduling for intraday demand within the power system. The analysis is conducted through rigorous modeling. Experimental tests were conducted to evaluate the performance of the proposed schemes. The day ahead dispatching primarily considers the impact of photovoltaic output and calculates the cost associated with each link in the grid under three different meteorological conditions. In the intra-day scheduling, the total costs for Scenario 1, Scenario 2, and Scenario 3 are found to be 34,724.5 yuan, 36,296.5 yuan, and 33,275.8 yuan, respectively. Notably, strategies 1 and 2 demonstrate lower costs compared to the pre-day scheduling, with the exception of Scenario 3. In real-time scheduling, considering the matching between sources and sources, the matching rate between sources and sources can be maintained at over 95%, and the stability and cost of the power grid have significantly decreased. In summary, by proposing a multi-stage time scale economic scheduling scheme, this study fully considers the uncertainty of the power supply of the distribution network access, as well as the different needs of day, day and real-time scheduling, providing an effective solution for the power dispatching of virtual power plants and providing important technical support for the reliability and economy of the power system.

On the cost of zero carbon electricity: A techno-economic analysis of combined cycle gas turbines with post-combustion CO2 capture

To achieve a sustainable electricity grid, affordable and dispatchable power capacity that supports grid stability and does not increase atmospheric CO2 levels will be required. Combined cycle gas turbines (CCGTs) with post-combustion CO2 capture (PCC) can meet these requirements by capturing and permanently storing all combustion CO2 emissions and, coupled with negative emissions technologies, any remaining life-cycle emissions. For the first time, we present a comprehensive analysis of the technical and financial challenges of producing zero-carbon electricity from CCGTs on a life-cycle basis. We conclude that when the design is optimised, fossil CO2 capture fractions can be increased from 96% to 100% with minimal process modification, commercially available technology and an additional 0.5% decrease in thermal efficiency. This represents a significant 60–70% reduction in the additional efficiency penalty required to reach zero CO2 emission operation. The Cost of CO2 Avoided of 100% fossil CO2 capture is just 128 £/tCO2, a 3 £/tCO2 increase over the 95% gross CO2 capture fraction required to permit PCC projects in the UK. Indeed, within the bounds of the UK power CCS business model, we show that 100% fossil CO2 capture maximises both variable and capacity payments to the producer, resulting in a 2% decrease in the cost of power produced. For zero-carbon electricity on a life-cycle basis, the recapture and permanent geological storage of all remaining life-cycle CO2e emissions from plant construction & decommissioning, CO2 T&S network operation and the natural gas supply chain (operational CO2e emissions and methane leakage) increases the median Cost of CO2 Avoided to 178 £/tCO2 (130–371 £/tCO2), leading to the novel conclusion that a market mechanism equating to a CO2 price of over 178£/tCO2 is likely sufficient to incentivise the development of truly CO2-neutral CCGTs.

Reactive Power Management in Loss and Voltage Control via Integrated Clonal Accelerated Evolutionary Programming

Optimal Reactive Power Dispatch (ORPD) plays a crucial role in maintaining voltage stability, enhance power transfer capability, improve system efficiency, regulate voltages, manage congestion, facilitate renewable energy integration, and achieve cost savings in power systems. This paper presents a new optimization technique for optimal reactive power management in power transmission system, called Integrated Clonal Accelerated Evolutionary Programming (ICAEP) algorithm to optimize the reactive power to be dispatched by the generator buses under 3 scenarios with cases. The ICAEP was developed based on hybridization of the traditional Artificial Immune System (AIS) and Evolutionary Programming (EP). The two cases demonstrate the effect of reactive load increment at the chosen load buses; while the three scenarios represent the ORPD schemes which indicate the involvement of generator buses either 3 generators, 4 generators or 5 generators. Implementation of ICAEP outperformed AIS and EP in both objective functions either in minimum voltage maximization or power loss minimization, involving all the cases under the three scenarios. The performance of the ICAEP is evaluated on the IEEE 30-bus test system. The results would be beneficial to power system operators and planning expansion and knowing the status of their systems in their utilities. The developed optimization engine is also robust and feasible for further optimization problem solving initiatives.

Intelligent Composite Compensation Under Load Variation in Power Transmission System.

The power transmission system is in a stressed condition due to the increasing electricity demand. This stress, exacerbated by the deregulated power system environment, necessitates an urgent additional supply to maintain system adequacy. One important initiative to alleviate the transmission burden which meets the load demand can be rectified by composite compensation scheme. This paper showcases intelligent composite compensation under varying loads, emphasizing loss minimization. The approach integrates a loss control scheme involving Distributed Generation (DG) and Optimal Reactive Power Dispatch (ORPD) with multi-DG installation termed composite compensation, employing a novel optimization technique called Integrated Cloning Accelerated Mutation Evolutionary Programming (ICAMEP). The study identifies optimal locations and sizes for composite compensation in the power transmission system, demonstrating its superiority over traditional optimization techniques namely the evolutionary programming (EP) and artificial immune systems (AIS). Results are demonstrated for four cases involving single DG, 3 DGs and composite compensation validated on IEEE 30-Bus Reliability Test System (RTS). ICAMEP is superior over EP and AIS in achieving the highest loss reduction.

Stochastic Dynamic Power Dispatch With Human Knowledge Transfer Using Graph-GAN Assisted Inverse Reinforcement Learning

Stochastic Dynamic Power Dispatch With Human Knowledge Transfer Using Graph-GAN Assisted Inverse Reinforcement Learning

Mid-to-long Term Wind Power Forecasting Based on ARIMA-BP Combined Model

Background: Increasing the accuracy of the output power forecasting for wind power is helpful to the improvement of the reliability of power dispatching. Objective: This study aimed to improve the forecasting accuracy of mid-to-long term wind power. Methods: A mid-to-long term wind power forecasting based on ARIMA-BP combined model was proposed. The Empirical Mode Decomposition (EMD) was used to decompose the historical wind power series and obtain the Intrinsic Mode Function (IMF) and residual components, thereby obtaining more regular components. Then, the optimum feature set was obtained based on the minimum Redundancy Maximum Relevance (mRAR) to improve the prediction accuracy for feature extraction. After that, the high-frequency components were predicted using the Back Propagation (BP) neural network model, while the low-frequency components were predicted using the Autoregressive Integrated Moving Average model (ARIMA). Finally, the predicted components obtained were superimposed to deduce the final mid- and long-term wind power prediction results. Results: An analysis was conducted according to the actual data from a typical wind farm. After comparison, it was found that, after empirical mode decomposition and feature extraction analysis, the error of the intelligent combination algorithm based on the ARIMA-BP combined model was smaller than that using only the BP neural network or only the ARIMA. Conclusion: By means of actual data analysis, the effectiveness of the method proposed by the study for mid- and long-term wind power prediction was verified.

Combined economic and emission power dispatch problems through multi-objective Honey Badger optimizer

Combined economic and emission power dispatch problems through multi-objective Honey Badger optimizer

Optimal sizing of hybrid PV–diesel–biomass gasification plants for electrification of off-grid communities: An efficient approach based on Benders’ decomposition

Nowadays, millions of people in remote areas do not enjoy an uninterrupted power supply due to the lack of connectivity to the main power grid. Under such circumstances, the only feasible way to access electricity is typically local power generation, often relying on diesel engine–generator sets or photovoltaic arrays. However, on many occasions, this configuration does not fully exploit all available local resources, including biomass. Indeed, most isolated areas have access to local biomass production from agricultural activities, which can be used for local electricity generation through gasification. This paper addresses this challenge by developing an innovative optimal sizing tool for hybrid power plants integrating biomass gasifiers, specifically designed for isolated areas with access to local biomass production. The novel approach models the particular features of biomass gasification technologies, including long on/off times or restrictive ramping limits. To this end, an efficient methodology based on representative weeks is proposed, which is combined with a solution strategy based on the multi-cut Benders’ decomposition, thus resulting in a tractable framework that can deal with a huge amount of data efficiently. One of the most salient features of the new proposal is the consideration of local biomass production, which is included in the methodology through an original algorithm. Accordingly, a certain amount of biomass is sourced locally, leading to more accurate and reliable results. The new methodology is applied to a benchmark off-grid community in Ghana. The results demonstrate that the use of gasifiers reduces the project cost notably (by 90%) driven by the reduced biomass cost, which can be supplemented by locally generated biomass from agricultural activities. In addition, this technology constitutes a clean source of energy, reducing the total CO2 emissions by 83% compared to a baseline case in which only diesel generators are used. Moreover, it is demonstrated that biomass gasification can effectively act as base load power generation technology to reliably cover most of the local demand, thereby enabling a clean and inexpensive dispatchable local power generation. Finally, a sensitivity analysis reveals that the economic feasibility of the plant is more sensitive to the biomass cost than the selling price of biochar, resulting in a 33% increment in the total project cost when the price of biomass increases from 0 to 0.4 $/kg. Nevertheless, gasification remains as the predominant power generation technology even under unfavorable prices.

An effective gradient jellyfish search algorithm for optimal reactive power dispatch in electrical networks

AbstractAn effective optimization technique, called gradient jellyfish search optimizer (GJSO), is introduced here to address the optimal reactive power dispatch (ORPD) issue in electric networks. The ORPD problem is a complex non‐linear optimization issue involving integrated variables, aimed at achieving safe and cost‐effective operation of the system by determining optimal values for generator voltage, tap changers of transformers, and reactive power compensation. The performance of the original JSO technique is enhanced by integrating the local escaping operator into the GJSO approach. The effectiveness of the GJSO methodology is evaluated via comparison with two existing methodologies: the original jellyfish search optimizer and the equilibrium optimizer. Jellyfish search optimizer is a meta‐heuristic optimization algorithm inspired by the movement of jellyfish in the water, while equilibrium optimizer draws inspiration from game theory and equilibrium concepts. Simulations were conducted using typical IEEE‐30 bus and IEEE‐57 bus systems to validate the performance of the GJSO methodology. Two versions of the objective function are examined: minimizing line power loss and minimizing total voltage deviations at the buses. The simulation results demonstrated that the GJSO algorithm exhibited superior performance in terms of accuracy and stability compared to the standard jellyfish search optimizer and equilibrium optimizer algorithms.

Integration of optimal power flow with combined heat and power dispatch of renewable wind energy based power system using chaotic driving training based optimization

Combined heat and power economic dispatch (CHPED) based optimal power flow (OPF) problem has been studied in this article using a new, practical approach based on chaotic driving training optimization (DTBO) (CDTBO). In the proposed technique (CDTBO), the chaotic based learning is integrated with DTBO to overcome the local optimal problem and inferior convergence speed of the existing algorithms. OPF is an important concern to retain the power system running effectively. In order to meet the demand for reasonably priced power generation with optimal power flow in transmission lines, the authors combined CHPED and OPF. Since fuel is changing daily in the current environment, using renewable energy sources to generate electricity economically is crucial. The renewable energy source like wind energy is integrated with thermal units for economic power generation with reducing the thermal fuel consumption of CHPED-based OPF system. The proposed technique implemented on CHPED based IEEE-30 bus system for renewable and without renewable energy sources with considering different cases. The suggested problem considering with valve point loading of thermal units, transmission losses and uncertainties of wind speed to address the non-linearity of the renewable-based CHPED-OPF system. Cost minimization, voltage deviation control, transmission losses minimization and stability index are the single objectives of the prospective system. Furthermore tested on multi-objective functions for simultaneously minimization of cost with emission and simultaneously minimization of active power loss with voltage profile. It is observed that the proposed CDTBO technique helps to reduce the cost by 2% and 12.8% for renewable based system as compared to non-renewable system for multi-objective function. The robustness of the proposed solution has been verified by implementing the statistical analysis on two systems with least variation of mean and optimal values of cost with the tolerance of less than 0.0035%. A comparison has been made with recent well known optimization techniques to address the superiority of the suggested CDTBO algorithm.

Gaussian Mutation Based Teaching-Learning Optimization for reactive power dispatch

This paper develops the Gaussian Mutation Based Teaching-Learning Optimization (GMBTLO) to solve different reactive power dispatch problems. In GMBTLO, Gaussian random variables are instituted, which includes both the ‘Teacher phase’ as well as ‘Learner phase’ helps to improve the efficiency of searching. Simultaneously, it ensures more feasibility to obtain the global optimal. Gaussian random variables are presented in GMBTLO, where both the ‘Teacher phase’ as well as ‘Learner phase’ is considered. This advances searching competence and guarantee a great possibility of achieving global optimum without affecting the convergence speed. In this paper, GMBTLO is implemented to enhance solution quality as well as convergence speed. Developed GMBTLO is exploited for obtaining the control variables settings including terminal voltages of a generator, transformer taps, as well as output power of shunt VAR compensators (reactive) for achieving a reduced loss in real power transmission and enhanced voltage profile. GMBTLO is validated by taking IEEE 30-bus, 57-bus along with 118-bus test systems. Also, test outcomes are matched with the results already acquired from other implemented evolutionary techniques. It is concluded that the suggested GMBTLO extends a better solution.

A framework for electricity load forecasting based on attention mechanism time series depthwise separable convolutional neural network

Electricity load exhibits daily and weekly cyclical patterns as well as random characteristics. At present, prevailing deep learning models cannot learn electricity load cyclical and stochastic features adequately. This results in insufficient prediction accuracy and the scalability of current methods. To tackle these difficulties, this paper proposes a framework for electrical load prediction based on an Attention Mechanism Time Series Depthwise Separable Convolutional Neural Network (ELPF-ATDSCN). The framework starts by using the Maximum Information Coefficient for exogenous variable selection. It then incorporates a seasonal decomposition algorithm with manual feature engineering to extract the cyclical and stochastic features of the electrical load. Subsequently, the framework employs the ATDSCN to learn the cyclical and stochastic features of the electrical load. In addition, the Bayesian algorithm optimizes model hyperparameters for optimal model performance. Experimental results of point and interval load prediction on datasets from the US and Nordic power markets reveal that the ATDSCN model proposed in this paper enhances load prediction accuracy compared with other models. It can provide more reliable predictions for power system operation and dispatch.