A Soft Computing-Based Analysis of Congestion Management in Transmission Systems

Congestion in the transmitting system is unitarily responsible for technological problems that appear, especially in a deregulated environment. The post-deregulation operation history of the electrical power system has placed greater pressure on the Independent System Operator (ISO) to assure a secure, congestion-less transmission network. Blackout and brownout voltage dip issues occur due to the heavy loading condition. Hence, this paper presents a novel approach for the relief of congestion by using a nature-inspired algorithm, namely Particle Swarm Optimization and Firefly Algorithm by considering various factors for re-dispatching active power of generators during overloading conditions. The algorithms are tested on IEEE 30 and IEEE 39 Bus standard test systems and the obtained results show the effectiveness of the proposed algorithm in the MATLAB environment. The congestion management (CoM) method is formulated as a constrained optimization problem with the objective function of relieving the overloading through minimization of factors such as Generator Shift Factor (GSF), Bus Sensitivity Factor (BSF), Line utilization Factor (LUF), and Congestion Index (CI). These factors are helpful to mitigate the transmission congestion, which in turn helps to reduce the real power losses

Optimal coordination of energy sources for microgrid incorporating concepts of locational marginal pricing and energy storage

This research aims to coordinate energy sources for standalone microgrid (MG), incorporating locational marginal pricing (LMP) and energy storage. Two approaches are suggested for the optimal energy management of MG. First, the energy management of a standalone MG is performed utilising the concept of LMP. The objective is to minimise the average LMP to reduce network congestion and power loss costs. Second, energy management is performed using a dual-stage energy management approach. A BESS model is formulated considering charging and discharging characteristics and utilised in this research for dual-stage energy management. The impact of the battery state of charge (SOC) is assessed in the optimal day-ahead operation. An incremental cost factor is included with battery SOC when calculating the system operating cost. A new binary jellyfish search algorithm (BJSA) is developed to solve energy management problems. The suggested BJSA technique is implemented in solving the optimal energy management of MG considering LMP. The simulations of the suggested approach are conducted on the IEEE 14 and 30-bus test systems. Results show that the BJSA technique is more consistent than the binary particle swarm optimisation (BPSO) technique in determining the optimal solution. In addition, the BJSA technique is employed to solve the dual-stage energy management of MG considering BESS. The proposed approach is simulated on the IEEE 14 and 30-bus systems. Results also show that the BJSA technique is superior to the BPSO technique in minimising the operating cost in real-time economic dispatch (ED). The performance of the BJSA and BPSO techniques is exactly similar to the UC schedule with and without BESS considering the IEEE 30-bus system, like the IEEE 14-bus system. The BJSA technique minimises operating costs by up to 5% over the BPSO technique for the UC schedule with power loss. Operating costs are reduced by up to 5% using the BJSA technique rather than the BPSO technique for real-time ED with BESS. However, the BPSO technique is inconsistent and fails to obtain the same results for the IEEE 30-bus system. Overall, the findings confirm the superiority of the suggested BJSA technique and the suggested optimisation approaches in optimising the energy management of MG

Enhancement of deregulated and restructured power network performance with flexible alternating current transmission systems devices.

Doctoral degree. University of KwaZulu- Natal, Durban.The increase in power transactions, consequent open access created by deregulation and restructuring has resulted into network operation challenges including determination as well as enhancement of available transfer capability (ATC), and congestion management among others. In this study, repeated alternating current power flow (RACPF) approach was implemented for determination of ATC. ATCs for inter-area line outage and generator outage contingency conditions were obtained and analyzed. Analyses of most severe line outage contingencies resulting from evaluation of different performance index (PI) ranking methods were carried out for severe line outage contingency identification. A comprehensive review of FACTS controllers with their various background, topological structures, deployment techniques and cutting-edge applications was carried out for network performance enhancement. In addition, different placement methods were investigated for optimal performance evaluation of FACTS devices. Following this, comparative performance of static var compensator (SVC) and thyristor-controlled series compensator (TCSC) models for enhancement of ATC, bus voltage profile improvement and real power loss minimization was investigated. In addition, particle swarm optimization (PSO) and brain-storm optimization algorithms (BSOA) were engaged for optimum setting of FACTS devices through multi-objective problem formulation and allocation purposes. Thereafter, sensitivity-based technique involving incorporation of proposed FACTS device loss with the general loss equation for the determination of optimum location with same objectives was developed and TCSC location was established based on this sensitivity factors analyses, obtained from partial derivatives of the resultant loss equations with respect to control parameters. Subsequently, investigation and analyses of capability of an optimized VSC-HVDC transmission system in enhancing power network performance were conducted. Furthermore, this optimized VSC-HVDC transmission system was applied for mitigation of bus voltage and line thermal limit violation as a result of n-1-line outage contingency. All these investigations and analyses were implemented for bilateral, simultaneous and multilateral transactions as characterized by network liberalization and IEEE 5 and 30 bus networks were used for implementation in MATLAB environment. RACPF method found to be more accurate especially when compared with other methods with 11.574 MW above and 29.014 MW below recorded ATC values. Voltage and real power PI have also been proven to be distinctly dissimilar in severe contingency identification. In placement method comparison however, disparities in ATC enhancement ranges between 2% and 85% were achieved while real power loss minimization of up to 25% was obtained for different methods. Real power loss minimization of up to 0.06 MW and voltage improvement of bus 21 to 30 were achieved with SVC, while ATC enhancement of up to 14% were recorded for both devices. However, BSO behaved much like PSO throughout the achievements of other set objectives but performed better in ATC enhancement with 27.12 MW and 5.24 MW increase above enhanced ATC values achieved by the latter. The comparison of set objectives values relative to that obtained with PSO methods depict suitability and advantages of BSOA technique. Sensitivity based placement technique resulted into ATC enhancement of more than 60% well above the values obtained when TCSC was placed with thermal limit method. In addition, a substantial bus voltage improvement and active power loss reduction were recorded with this placement method. With incorporation of a VSC-HVDC based transmission system into ac network however, there was an improvement in power flow up to 15.66% corresponding to 46 MW for various transactions, transmission line power loss minimization up to 0.38 MW and bus voltage profile deviation minimization. Besides, automatic alleviation of violated thermal and voltage limits during contingency present VSC-HVDC system as a solution for network performance optimization especially during various transactions occasioned by unbundling power processes. Therefore, ATCs were properly enhanced, bus voltage profile improved, and system real power loss minimized. Likewise, HVDC system enhanced network performance and automatically alleviated violated thermal and voltage limits during contingency

Hybrid artificial intelligence algorithms for short-term load and price forecasting in competitive electric markets

The liberalization and deregulation of electric markets forced the various participants to accommodate several challenges, including: a considerable accumulation of new generation capacity from renewable sources (fundamentally wind energy), the unpredictability associated with these new forms of generation and new consumption patterns, contributing to further electricity prices volatility (e.g. the Iberian market). Given the competitive framework in which market participants operate, the existence of efficient computational forecasting techniques is a distinctive factor. Based on these forecasts a suitable bidding strategy and an effective generation systems operation planning is achieved, together with an improved installed transmission capacity exploitation, results in maximized profits, all this contributing to a better energy resources utilization. This dissertation presents a new hybrid method for load and electricity prices forecasting, for one day ahead time horizon. The optimization scheme presented in this method, combines the efforts from different techniques, notably artificial neural networks, several optimization algorithms and wavelet transform. The method’s validation was made using different real case studies. The subsequent comparison (accuracy wise) with published results, in reference journals, validated the proposed hybrid method suitability.O processo de liberalização e desregulação dos mercados de energia elétrica, obrigou os diversos participantes a acomodar uma série de desafios, entre os quais: a acumulação considerável de nova capacidade de geração proveniente de origem renovável (fundamentalmente energia eólica), a imprevisibilidade associada a estas novas formas de geração e novos padrões de consumo. Resultando num aumento da volatilidade associada aos preços de energia elétrica (como é exemplo o mercado ibérico). Dado o quadro competitivo em que os agentes de mercado operam, a existência de técnicas computacionais de previsão eficientes, constituí um fator diferenciador. É com base nestas previsões que se definem estratégias de licitação e se efetua um planeamento da operação eficaz dos sistemas de geração que, em conjunto com um melhor aproveitamento da capacidade de transmissão instalada, permite maximizar os lucros, realizando ao mesmo tempo um melhor aproveitamento dos recursos energéticos. Esta dissertação apresenta um novo método híbrido para a previsão da carga e dos preços da energia elétrica, para um horizonte temporal a 24 horas. O método baseia-se num esquema de otimização que reúne os esforços de diferentes técnicas, nomeadamente redes neuronais artificiais, diversos algoritmos de otimização e da transformada de wavelet. A validação do método foi feita em diferentes casos de estudo reais. A posterior comparação com resultados já publicados em revistas de referência, revelou um excelente desempenho do método hibrido proposto

An efficient and reliable scheduling algorithm for unit commitment scheme in microgrid systems using enhanced mixed integer particle swarm optimizer considering uncertainties

The use of an electrical energy storage system (EESS) in a microgrid (MG) is widely recognized as a feasible method for mitigating the unpredictability and stochastic nature of sustainable distributed generators and other intermittent energy sources. The battery energy storage (BES) system is the most effective of the several power storage methods available today. The unit commitment (UC) determines the number of dedicated dispatchable distributed generators, respective power, the amount of energy transferred to and absorbed from the microgrid, as well as the power and influence of EESSs, among other factors. The BES deterioration is considered in the UC conceptualization, and an enhanced mixed particle swarm optimizer (EMPSO) is suggested to solve UC in MGs with EESS. Compared to the traditional PSO, the acceleration constants in EMPSO are exponentially adapted, and the inertial weight in EMPSO decreases linearly during each iteration. The proposed EMPSO is a mixed integer optimization algorithm that can handle continuous, binary, and integer variables. A part of the decision variables in EMPSO is transformed into a binary variable by introducing the quadratic transfer function (TF). This paper also considers the uncertainties in renewable power generation, load demand, and electricity market prices. In addition, a case study with a multiobjective optimization function with MG operating cost and BES deterioration defines the additional UC problem discussed in this paper. The transformation of a single-objective model into a multiobjective optimization model is carried out using the weighted sum approach, and the impacts of different weights on the operating cost and lifespan of the BES are also analyzed. The performance of the EMPSO with quadratic TF (EMPSO-Q) is compared with EMPSO with V-shaped TF (EMPSO-V), EMPSO with S-shaped TF (EMPSO-S), and PSO with S-shaped TF (PSO-S). The performance of EMPSO-Q is 15%, 35%, and 45% better than EMPSO-V, EMPSO-S, and PSO-S, respectively. In addition, when uncertainties are considered, the operating cost falls from $8729.87 to$8986.98. Considering BES deterioration, the BES lifespan improves from 350 to 590, and the operating cost increases from $8729.87 to$8917.7. Therefore, the obtained results prove that the EMPSO-Q algorithm could effectively and efficiently handle the UC problem

Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems

The electrical power system is undergoing a revolution enabled by advances in telecommunications, computer hardware and software, measurement, metering systems, IoT, and power electronics. Furthermore, the increasing integration of intermittent renewable energy sources, energy storage devices, and electric vehicles and the drive for energy efficiency have pushed power systems to modernise and adopt new technologies. The resulting smart grid is characterised, in part, by a bi-directional flow of energy and information. The evolution of the power grid, as well as its interconnection with energy storage systems and renewable energy sources, has created new opportunities for optimising not only their techno-economic aspects at the planning stages but also their control and operation. However, new challenges emerge in the optimization of these systems due to their complexity and nonlinear dynamic behaviour as well as the uncertainties involved.This volume is a selection of 20 papers carefully made by the editors from the MDPI topic “Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems”, which was closed in April 2022. The selected papers address the above challenges and exemplify the significant benefits that optimisation and nonlinear control techniques can bring to modern power and energy systems

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

AI Applications to Power Systems

Today, the flow of electricity is bidirectional, and not all electricity is centrally produced in large power plants. With the growing emergence of prosumers and microgrids, the amount of electricity produced by sources other than large, traditional power plants is ever-increasing. These alternative sources include photovoltaic (PV), wind turbine (WT), geothermal, and biomass renewable generation plants. Some renewable energy resources (solar PV and wind turbine generation) are highly dependent on natural processes and parameters (wind speed, wind direction, temperature, solar irradiation, humidity, etc.). Thus, the outputs are so stochastic in nature. New data-science-inspired real-time solutions are needed in order to co-develop digital twins of large intermittent renewable plants whose services can be globally delivered

Application of Power Electronics Converters in Smart Grids and Renewable Energy Systems

This book focuses on the applications of Power Electronics Converters in smart grids and renewable energy systems. The topics covered include methods to CO2 emission control, schemes for electric vehicle charging, reliable renewable energy forecasting methods, and various power electronics converters. The converters include the quasi neutral point clamped inverter, MPPT algorithms, the bidirectional DC-DC converter, and the push–pull converter with a fuzzy logic controller