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Microgrid systems are becoming a very promising solution to meet the power demand growth especially in remote areas where diesel generators (DG) are commonly used as a main energy source. Photovoltaic (PV) systems are commonly used as a sustainable energy source to economize DG fuel. Due to the intermittent and fluctuating behavior of PV generators, energy storage systems (ESS) such as electrochemical battery are suggested. PV and ESS are usually connected using one inverter/charger called hybrid inverter. The power management is crucial to optimize the fuel consumption and operate efficiently ESS. Additionally, in an off-grid operation, the microgrid frequency becomes sensible due to the slow dynamic of DG which requires an additional control tool to improve the frequency regulation. This paper proposes a new power management based on Mamdani fuzzy logic. The proposed controller considers the targets mentioned above by only controlling the hybrid inverter. Simulation results prove that fuzzy-based controller reduces the DG fuel consumption by more than 12% compared to classical hysteresis management control. Moreover, the proposed controller performs efficiently regarding the conventional frequency regulation, which is widely used in microgrid control.

In recent times, the energy consumed by buildings facilities became considerable. Efficient local energy management is vital to deal with building power demand penalties. This operation becomes complex when a hybrid energy system is included in the power system. This study proposes new energy management between photovoltaic (PV) system, Battery Energy Storage System (BESS) and the power network in a building by controlling the PV/BESS inverter. The strategy is based on explicit model predictive control (MPC) to find an optimal power flow in the building for one-day ahead. The control algorithm is based on a simple power flow equation and weather forecast. Then, a cost function is formulated and optimised using genetic algorithms-based solver. The objective is reducing the imported energy from the grid preventing the saturation and emptiness of BESS. Including other targets to the control policy as energy price dynamic and BESS degradation, MPC can optimise dramatically the efficacy of the global building power system. The strategy is implemented and tested successfully using MATLAB/SimPowerSystems software, compared to classical hysteresis management, MPC has given 10% in energy cost economy and 25% improvement in BESS lifetime.

The work presented in this article concerns the issues of the integration of renewable energies and in particular photovoltaic solar energy into distribution networks. In this context we used the grid of the city of Djanet-Algeria to study the impact of the injection of a 3MW photovoltaic power plant and to visualize its influence on the different main parameters of the grid which are: voltage, active and reactive losses, and frequency. Simulations are performed using the CYME distribution network simulation tool. The analysis of the different scenarios executed using this tool, we were able to study the static and dynamic behavior of the integration of a photovoltaic (PV) plant and to find the optimal location for it.

This study proposes a solution to the optimal reactive power flow (ORPF) in power systems incorporating wind power. Two aspects are studied in this paper. The first one is the technical aspect, includes the real power loss reduction, which presents the main objective of this work. The second is the economic aspect, considers the optimal wind power and FACTS devices investment cost. The autonomous group particle swarm optimization (AGPSO) optimization technique is employed and compared with improved and modified particle swarm optimization (IPSO & MPSO) for solving the proposed ORPF problem. The Algerian power system 59-bus systems are used as test systems. The simulation results obtained from the proposed algorithm shows that this algorithm is capable to give higher quality solutions to solve the optimal reactive power flow problem.

This paper proposes a multi-objective method-ology for optimal placement of Phasor Measurement Units (PMUs) for small-signal stability assessment of a power system, through Prony analysis on the voltage signals measured by the PMUs, two objectives are considered: the optimal location with a minimum number of PMU and maximum SNR. The main idea behind the paper, is to use a multi-objective Genetic algorithm (MOGA) to find a good estimation of the eigen-values (frequency and damping) of both local and inter-area electromechanical oscillations in a power system, by finding the optimal number and location of PMUs giving system full observability and maximum signal-to-noise ratio (SNR). The proposed algorithm is tested in the IEEE 39-bus system, and the results have been validated with modal analysis from PSAT toolbox.

—Research in Microgrid has taken recently more attentions in the context of solving the conventional centralized power network constraints. Combining information and power technologies, the efficiency and reliability of the power network have been improved dramatically. As an additional advantage, including renewable energy sources and storage in to form distributed energy systems has proven its aptitude to solved some economic and technical penalties appearing in classical centralized systems. However, appropriate energy management algorithms in hybrid microgrids are primordial to reach the objectives mentioned previously, for which, this paper has given a significant contribution. This work proposes a practical and efficient solution to implement a real-time energy management policy on a microgrid prototype based on commercial products. This method is implemented and tested successfully to manage the power flow between 6.3 KWp photovoltaic system; 4.5 KW lead-acid batteries and 3.6 KW programmable loads. The objective is to maximize the use of renewable energy while driving efficiently the storage system. Another target summarized in operating the microgrid in island mode as much as possible to improve its reliability.

This paper present several cases of study. The network used is the IEEE 10 generators in two different seasons, summer and winter. Two metaheuristic methods were chosen and examined (GA and PSO) for the study of the optimization processed in this paper, which presents the minimization of photovoltaic costs, NOx emission cost, and the cost of the fuel with the effect of the valve.

Differential evolution algorithm (DEA) is an efficient and powerful population-based stochastic search technique for solving optimization problems over continuous space, which has been proved to be a promising evolutionary algorithm for solving the ORPD problem and many engineering problems. However, the success of DEA in solving a specific problem crucially depends on appropriately choosing trial vector generation strategies (mutation strategies) and their associated control parameter values. This paper presents a differential evolution technique with various trial vector generation strategies based on optimal reactive power dispatch for real power loss minimization in power system. The proposed methodology determines control variable settings such as generator terminal voltages, tap positions and the number of shunts compensator to be switched, for real power loss minimization in the transmission systems. The DE method has been examined and tested on the IEEE 14-bus, 30-bus and the equivalent Algerian electric 114-bus power system. The obtained results are compared with two other methods, namely, interior point method (IPM), Particle Swarm Optimization (PSO) and other methods in the literature. The comparison study demonstrates the potential of the proposed approach and shows its effectiveness and robustness to solve the ORPD problem.

A number of investigations were undertaken to enhance the behavior of high voltage outdoor insulators by adopting numerical methods of optimization, but no work is performed to account for the presence of pollution. In this paper, a shape optimization of a high voltage insulator is achieved with the objective of reducing the tangential electric field along its polluted surface by means of numerical methods, namely neural networks and genetic algorithms.

The grid is hierarchical in three parts whose functions are quite different. First, the transmission system's role is to transport energy into high voltage from production centers to consumption areas and distribution network directly supplies large industrial consumers and supplying the average and low voltage consumers, with the latest advances in industry and Technological the need for electricity is increasing. These developments require the development of distribution network to optimize the balance between supply and demand. A very large part of the power losses and deviation voltage problems in power systems are assigned to distribution network, where power losses are estimated at 14 %. So, this paper investigates the use of the multi objective Differential Evolution algorithms developed by Matlab to find the optimal distribution network reconfiguration minimizing power losses and improve voltage deviation. To test the effectiveness of the proposed algorithm, three IEEE distribution systems respectively 33 buses, 69 busses and 123 busses are used for two objectives functions and the results are being compared.

This work proposes and simulates a finite set model predictive control technique for the photovoltaic inverter. The power topology is a 30 KW PV array with two power conversion stages: DC/DC boost converter controlled with maximum power point tracking to extract the maximum available photovoltaic power and two-level voltage source inverter with passive filter to convert direct current to alternative current and inject it into a medium voltage grid via a transformer, a 30 KW lead-acid batteries system connected to the inverter DC bus through buck-boost converter. Two control levels appear for the inverter: a simple hysteresis-based power management which sends the power reference to the second level according to the battery state of charge value. The second level is a finite set model predictive controller which makes the inverter inject desired high-quality currents according to the reference. This technique doesn't need a modular for switching and the digital simulation with MATLAB/Simulink software proves the performance of MPC controller in term of injected currents quality with THD value of 0.91% compared to conventional hysteresis controller with THD of 1.57%.

This paper investigates the use of competitive coevolutionary algorithms to calculate suppliers’ optimal strategies in a deregulated electricity market. The two settlement model is used, consisting of a spot and a forward market. Agents can take part in both spot and forward transactions, and act strategically to maximise their profit from both markets. The strategic interactions of market agents are modelled as a non cooperative game. The competitive coevolutionary Algorithm is used to calculate the Nash Equilibrium strategies ensuring the best outcome for each agent. Results demonstrates the effectiveness of the coevolutionary approach to find the optimal strategies in different market situations.

In this paper, the interior point method (IPM) is applied to the optimal reactive power planning (ORPP) problem using the thyristor controlled series compensator (TCSC) device with consideration of voltage stability with handling the improvement of transmission line capacity in electrical network. To improve this case of study, two other objective functions are associated. The first is the angle deviation minimisation problem; it minimises the difference between the sending and receiving bus angles. And the second one is voltage deviation problem which aims to improve the system voltage profiles. The choice for these associate objective functions is defined by the most influence of bus angles and voltages to improve the system transmission line capacity. Also, the proposed algorithms satisfy various constraints, which are: power flow equations, generator voltage limits, switchable capacitor banks, transformer tap changer limits and transmission line limits. The fast voltage stability index (FVSI) is used to identify the critical lines to install the TCSC controllers. The methodology has been tested in the equivalent Algerian electrical power systems 114 bus. The simulation results show the effectiveness of the proposed approach for improving the reactive power planning problem.

The ongoing changes in deregulated electricity markets creates a need of new tools for the analysis and simulation of electricity markets. The current paper proposes a new methodology based on neuroevolution algorithm for the analysis of a day-ahead electricity market. In this work, we consider a multi-period electricity market where market agents (suppliers and consumers) have to submit their offers for each hour of the trading day. A deregulated electricity market is an example of a stochastic game, market agents have to compete among themselves, in an uncertain environment, in order to maximize their profits from the day ahead transactions. A case study based on IEEE30 bus system is used to demonstrate the ability of neuroevolution algorithms as a powerful tools for the analysis of deregulated electricity markets. The proposed approach combines the ability of neural network to learn from a stochastic environment, and the optimization capability of coevolutionary algorithms to find the best strategies for market agents.

In this paper, a competitive coevolutionary approach is used to find out the Nash–Cournot equilibrium of an oligopolistic electricity market based on either centralized or bilateral structure, where the transmission constraints are considered. We consider a market game played by bounded evolutionary agents, adapting their strategies to maximize their profits in a competitive environment. Under the effect of the competition, the coevolved agents are led toward an equilibrium point where each agent population is invaded by an Evolutionary stable Strategy.

The increasing presence of green energy on grid needs using of high control strategy and good power quality. For that reason this paper presents analysis and control of three phase space vector pulse width modulated (SVPWM) two level voltage source inverters (VSI) with constant switching frequency using a direct power control (DPC). In order to improve the power quality by reducing harmonic distortion, a design of LCL filter by genetic algorithm (GA) and particle swarm optimization (PSO) is also described. Comparative studies between these two methods are discussed to show the most efficient one in terms of generation and harmonics injected to the grid.

Now a day the transmission lines are operated under the heavily stressed condition, hence there is risk of the consequent voltage instability. This paper proposes a particle swarm optimization method for solving optimal reactive power planning (ORPP) problem using Thyristor Controlled Series Compensator (TCSC). The proposed PSO have been applied for the ORPP problem to improve the system line stability by minimizing the line stability index (Lmn) and to minimize the investment cost of TCSC devices, satisfying various constraints of power flow equation, generator voltage limits, TCSC's reactance limits, transformer tap changer limits, and transmission line limits. The Line Stability Index (Lmn) are used to identify the stressed lines which will receive the FACTS devices (TCSC). The proposed method has been examined and tested on the Algerian electric power system 114-bus and the obtained results are compared with two other methods, namely, Genetic Algorithm (GA), interior point method (IPM). The result comparisons demonstrate the potential of the proposed approach and show its effectiveness and robustness to solve the ORPP problem. Therefore, the proposed PSO can be a promising solution method for dealing the ORPP problem.

the main purpose of the paper is to calculate supplier's optimal biding in a deregulated electricity market, by calculating the Nash equilibrium strategies. In this paper we present the use of competitive coevolutionary algorithm in order to find the optimal biding strategies. A computational Algorithm has been developed to find Nash equilibrium strategies where a stochastic programming model is proposed to maximize the expected profits taking into account the stochastic aspect of spot market parameters. The key feature of our approach is the combination of a powerful learning algorithm to find the optimal strategies, and a scenario formulation to model the market uncertainties through. Each market agents is modeled as an adaptive evolutionary agent learning from market interactions and take part in the forward and spot transactions to act strategically to maximize their profits.

The present paper investigates the use of competitive coevolutionary algorithm in order to find supplier's optimal strategies in a Day-Ahead Electricity Market. Market game was expressed as an optimization problem based on the definition of Nash Equilibrium strategies where the game is playing by evolutionary agents, adapting theirs strategies to maximize the profits in a competitive environment. Day-ahead market where the IS0 uses a daily load profile submitted by each Service Load to perform a DC-OPF to determine Location Marginal Price and quantities bids from each supplier generators. Market agents' (suppliers) take part in the Day-ahead transactions and act strategically in order to increase their profits from each hourly transaction.

Market equilibrium of an oligopolistic electricity market considering transmission constraints. The market game is played by bounded evolutionary agents, adapting their strategies to maximize their profits in a competitive environment. This paper considers a centralized market with maximalist ISO. The ISO performs an OPF based on submitted Agentspsila bid and calculates the power to be dispatched from each supplier, in order to maximize the social welfare and preserve the integrity of the power system, while, market agents interact strategically to maximize their profit. By using competitive coevolutionary algorithm as a learning algorithm, agentspsila strategies are led towards Nash-Cournot equilibrium, where no agent is incited to change unilaterally his strategy.

The present paper investigates the use of competitive coevolutionary algorithm in order to find supplier's optimal strategies in a deregulated electricity market, by calculating the Nash equilibrium strategies. In this work, we consider a stochastic model where spot market parameters are not known in advance. the market agents can take part in the forward and spot transactions and act strategically in order to increase their profits from both transactions.