Renewable Energy Resources Technologies and Life Cycle Assessment: Review

Moving towards RER has become imperative to achieve sustainable development goals (SDG). Renewable energy resources (RER) are characterized by uncertainty whereas, most of them are unpredictable and variable according to climatic conditions. This paper focuses on RER-based electrical power plants as a base to achieve two different goals, SDG7 (obtaining reasonably priced clean energy) and SDG13 (reducing climate change). These goals in turn would support other environmental, social, and economic SDG. This study is constructed based on two pillars which are technological developments and life cycle assessment (LCA) for wind, solar, biomass, and geothermal power plants. To support the study and achieve the main point, many essential topics are presented in brief such as fossil fuels’ environmental impact, economic sustainability linkage to RER, the current contribution of RER in energy consumption worldwide and barriers and environmental effects of RER under consideration. As a result, solar and wind energy lead the RER electricity market with major contributions of 27.7% and 26.92%, respectively, biomass and geothermal are still of negligible contributions at 4.68% and 0.5%, respectively, offshore HAWT dominated other WT techniques, silicon-based PV cells dominated other solar PV technologies with 27% efficiency, combustion thermochemical energy conversion process dominated other biomass energy systems techniques, due to many concerns geothermal energy system is not preferable. Many emerging technologies need to receive more public attention, intensive research, financial support, and governmental facilities including effective policies and data availability

Renewable Energy Resources Technologies and Life Cycle Assessment: Review

Moving towards RER has become imperative to achieve sustainable development goals (SDG). Renewable energy resources (RER) are characterized by uncertainty whereas, most of them are unpredictable and variable according to climatic conditions. This paper focuses on RER-based electrical power plants as a base to achieve two different goals, SDG7 (obtaining reasonably priced clean energy) and SDG13 (reducing climate change). These goals in turn would support other environmental, social, and economic SDG. This study is constructed based on two pillars which are technological developments and life cycle assessment (LCA) for wind, solar, biomass, and geothermal power plants. To support the study and achieve the main point, many essential topics are presented in brief such as fossil fuels’ environmental impact, economic sustainability linkage to RER, the current contribution of RER in energy consumption worldwide and barriers and environmental effects of RER under consideration. As a result, solar and wind energy lead the RER electricity market with major contributions of 27.7% and 26.92%, respectively, biomass and geothermal are still of negligible contributions at 4.68% and 0.5%, respectively, offshore HAWT dominated other WT techniques, silicon-based PV cells dominated other solar PV technologies with 27% efficiency, combustion thermochemical energy conversion process dominated other biomass energy systems techniques, due to many concerns geothermal energy system is not preferable. Many emerging technologies need to receive more public attention, intensive research, financial support, and governmental facilities including effective policies and data availability

Two-stage grid-connected inverter topology with high frequency link transformer for solar PV systems

This study introduces a new topology for a single-phase photovoltaic (PV) grid connection. This suggested topology comprises two cascaded stages linked by a high-frequency transformer. In the first stage, a new buck–boost inverter with one energy storage is implemented. The buck–boost inverter can convert the PV module’s output voltage to a high-frequency square wave (HFSWV) and can enhance maximum power point tracking (MPPT) even under large PV voltage variations. The high-frequency transformer gives galvanic isolation for the system, which decreases the leakage current and improves the system power quality. The second stage of the topology involves using a rectifier-inverter system to interface the produced HFSWV to the utility grid. The proposed system uses high switching frequency which increases the power density, reduces the grid filter size, and increases the system reliability. Buck–boost DC/AC inversion, MPPT and low grid current injection are implemented. The working principles of the proposed topology have been investigated, and the theoretical and experimental results are developed and analyzed

Reactive Power Management Based Hybrid GAEO

Electrical power networks are expanded regularly to meet growing energy requirements. Reactive power dispatch (RPD) optimization is a powerful tool to enhance a system’s efficiency, reliability, and security. RPD optimization is classified as a non-linear and non-convex problem. In this paper, the RPD optimization problem is solved based on novel hybrid genetic algorithms—equilibrium optimizer (GAEO) optimization algorithms. The control variables are determined in such a way that optimizes RPD and minimizes power losses. The efficiency of the proposed optimization algorithms is compared to other techniques that have been used recently to solve the RPD problem. The proposed algorithm has been tested for optimization RPD for three test systems, IEEE14-bus, IEEE-30bus, and IEEE57-bus. The obtained results show the superiority of GAEO over other techniques for small test systems, IEEE14-bus and IEEE-30bus. GAEO shows good results for large system, IEEE 57-bus

Reactive Power Management Based Hybrid GAEO

Electrical power networks are expanded regularly to meet growing energy requirements. Reactive power dispatch (RPD) optimization is a powerful tool to enhance a system’s efficiency, reliability, and security. RPD optimization is classified as a non-linear and non-convex problem. In this paper, the RPD optimization problem is solved based on novel hybrid genetic algorithms—equilibrium optimizer (GAEO) optimization algorithms. The control variables are determined in such a way that optimizes RPD and minimizes power losses. The efficiency of the proposed optimization algorithms is compared to other techniques that have been used recently to solve the RPD problem. The proposed algorithm has been tested for optimization RPD for three test systems, IEEE14-bus, IEEE-30bus, and IEEE57-bus. The obtained results show the superiority of GAEO over other techniques for small test systems, IEEE14-bus and IEEE-30bus. GAEO shows good results for large system, IEEE 57-bus

An Online Archimedes Optimization Algorithm Identifier-Controlled Adaptive Modified Virtual Inertia Control for Microgrids

Single widespread employment of renewable energy sources (RESs) contributes to the shortage in the inertia of the microgrid (MG). After this, frequency stability may regress as a result of power imbalance or minor load fluctuations. This paper proposes an explicit adaptive modified virtual inertia control (VIC) based on an online Archimedes optimization algorithm (AOA) identifier for MG containing thermal, wind, and solar photovoltaic power plants. The Rung Kutta approach is used to construct the proposed online identifier, which acts as a model of the MG. AOA predicts the coefficients of the online identifier based on the input and output of MG to mimic the frequency deviation of the MG online. AOA estimates online the inertia and damping coefficients of the VIC system with an energy storage device based on online AOA identifier coefficients. The frequency deviation of the MG based on the proposed explicit adaptive modified VIC is compared with the conventional VIC based on fixed parameters and the VIC system based on optimal parameters using AOA offline under mutation in loads, weather-dependent input, and MG parameters using MATLAB/Simulink software. Furthermore, the proposed explicit adaptive modified VIC based on an online AOA identifier is evaluated with the adaptive VIC system based on fuzzy logic control, which adjusts only the inertial gain online. The simulation results demonstrate the capabilities of the proposed explicit adaptive modified VIC to improve the frequency stability and enhance low-inertia islanded MGs with RESs

Optimal Sizing of a Real Remote Japanese Microgrid with Sea Water Electrolysis Plant Under Time-Based Demand Response Programs

Optimal sizing of power systems has a tremendous effective role in reducing the total system cost by preventing unneeded investment in installing unnecessary generating units. This paper presents an optimal sizing and planning strategy for a completely hybrid renewable energy power system in a remote Japanese island, which is composed of photovoltaic (PV), wind generators (WG), battery energy storage system (BESS), fuel cell (FC), seawater electrolysis plant, and hydrogen tank. Demand response programs are applied to overcome the performance variance of renewable energy systems (RESs) as they offer an efficient solution for many problems such as generation cost, high demand peak to average ratios, and assist grid reliability during peak load periods. Real-Time Pricing (RTP), which is deployed in this work, is one of the main price-based demand response groups used to regulate electricity consumption of consumers. Four case studies are considered to confirm the robustness and effectiveness of the proposed schemes. Mixed-Integer Linear Programming (MILP) is utilized to optimize the size of the system’s components to decrease the total system cost and maximize the profits at the same time

Optimal Multi-Objective Power Scheduling of a Residential Microgrid Considering Renewable Sources and Demand Response Technique

Microgrid optimization is one of the most promising solutions to power system issues and new city electrification. This paper presents a strategy for optimal power scheduling of a residential microgrid depending on renewable generating sources and hydrogen power. Five scenarios of the microgrid are introduced to show the effect of using biomass energy and a seawater electrolyzer on microgrid cost and CO2 emissions. Time of use demand response is applied to reshape the electric load demand and decrease the dependence on grid power. The obtained results from the multi-objective optimization verify that biomass has a significant role in minimizing the cost and CO2 emissions; the cost is decreased by 37.9% when comparing scenarios with and without biomass. Besides, the FC integration with seawater electrolyzer and tanks reduces the microgrid emissions by around 40%

Applications of hybrid model predictive control with computational burden reduction for electric drives fed by 3-phase inverter

Model predictive control (MPC) is recently emerging as an efficient and promising technique for the control of power converters. In the conventional MPC algorithm, the control objectives are usually estimated and evaluated for a large/definite number of switching states. Since prediction and evaluation are done for all possible states, massive amounts of estimations are needed, moreover, the computational burden is more challenging with the increase of control objectives. In this paper, a computationally efficient version of the finite control set-MPC (FCS-MPC) is proposed to decrease the calculation effort of the MPC algorithm likewise minimizing its execution time to enforce its vast application for the control of three-phase power converters. The suggested procedure is to eliminate the current predictions as well as reduce the number of available switching states that need to be estimated by the algorithm which reduces considerably the amount of time consumed by these computations. The studied techniques achieved nearly the same performance with an interesting reduction in the algorithm execution time accomplished by the proposed modified FCS-MPC algorithms

Modern Temperature Control of Electric Furnace in Industrial Applications Based on Modified Optimization Technique

In this paper, an enhanced version of whale optimization algorithm (EWOA) is presented to be applied in adaptive control techniques as a parameter tuner. One weakness point in this control scheme is the low efficiency of its objective function. Balloon effect (BE) is a modification introduced to increase the efficiency of the objective function of the optimization method and the ability of the controller to deal with system problems increase consequently. Controlling of the temperature of electric furnaces is considered as one of the important issues in several industrial applications. Conventional controllers such as PID controller cannot deal efficiently with the problem of parameters variations and step disturbance. This paper proposes an adaptive controller, in which the gain of the temperature controller is tuned online using EWOA supported by balloon effect. System responses obtained by the proposed adaptive control scheme using EWOA + BE have been compared with an electric furnace temperature control (EFTC) scheme response using both the PID controller-based modified flower pollination algorithm (MoFPA) and PID-accelerated PIDA-based MoFPA. From the results, it can be observed that the proposed controller tuned by the EWOA + BE method improves the time performance compared with the other techniques (PID and PIDA-based MoFPA) in case of EFTC application