Renewable Energy Sources For Power Generation Research Articles

Smart optimization in battery energy storage systems: An overview

The increasing drive towards eco-friendly environment motivates the generation of energy from renewable energy sources (RESs). The rising share of RESs in power generation poses potential challenges, including uncertainties in generation output, frequency fluctuations, and insufficient voltage regulation capabilities. As a solution to these challenges, energy storage systems (ESSs) play a crucial role in storing and releasing power as needed. Battery energy storage systems (BESSs) provide significant potential to maximize the energy efficiency of a distribution network and the benefits of different stakeholders. This can be achieved through optimizing placement, sizing, charge/discharge scheduling, and control, all of which contribute to enhancing the overall performance of the network. In this paper, we provide a comprehensive overview of BESS operation, optimization, and modeling in different applications, and how mathematical and artificial intelligence (AI)-based optimization techniques contribute to BESS charging and discharging scheduling. We also discuss some potential future opportunities and challenges of the BESS operation, AI in BESSs, and how emerging technologies, such as internet of things, AI, and big data impact the development of BESSs.

Application of whale algorithm optimizer for unified power flow controller optimization with consideration of renewable energy sources uncertainty

Purpose. In this paper an allocation methodology of Flexible Alternating Current Transmission Systems (FACTS) controllers, more specifically, the Unified Power Flow Controller (UPFC) is proposed. As the penetration of Renewable Energy Sources (RESs) into the conventional electric grid increases, its effect on this location must be investigated. Research studies have shown that the uncertainty of RESs in power generation influences the reactive power of a power system network and consequently its overall transmission losses. The novelty of the proposed work consists in the improvement of voltage profile and the minimization of active power loss by considering renewable energy sources intermittency in the network via optimal location of UPFC device. The allocation strategy associates the steady-state analysis of the electrical network, with the location and adjustment of controller parameters using the Whale Optimization Algorithm (WOA) technique. Methodology. In order to determine the location of UPFC, approaches are proposed based on identification of a line which is the most sensitive and effective with respect to voltage security enhancement, congestion alleviation as well as direct optimization approach. The optimum location of UPFC in the power system is discussed in this paper using line loading index, line stability index and optimization method. The objective function is solved using the WOA algorithm and its performance is evaluated by comparison with Particle Swarm Optimization (PSO) algorithm. Results. The effectiveness of the proposed allocation methodology is verified through the analysis of simulations performed on standard IEEE 30 bus test system considering different load conditions. The obtained results demonstrate that feasible and effective solutions are obtained using the proposed approach and can be used to overcome the optimum location issue. Additionally, the results show that when UPFC device is strategically positioned in the electrical network and uncertainty of RES is considered, there is a significant influence on the overall transmission loss and voltage profile enhancements of the network.

Criterion modelling of the process of redundancy of renewable energy sources power generation instability by electrochemical accumulators

The paper analyzes the methods and ways for the redundancy of renewable energy sources (RESs) instability in electric power systems (PS). It is shown that these can be maneuverable capacities, in particular, thermal and hydropower plants, accumulators, hydrogen technologies, biogas plants. It is substantiated that, for various reasons, for the RESs to be developed and their capacity to be increased in power systems, electrochemical accumulators and highly maneuverable capacities existing in the PS are the most prepared for implementation. Mathematical models based on similarity theory and the criterion method have been developed for them. This approach is preferable due to the fact that with the minimum available information, it provides the opportunity to compare different ways of compensation of RESs generation instability, to assess their proportionality, as well as to determine the sensitivity of costs to the capacity of the redundancy methods. Criterion models which allow us to design dependences of the costs for the redundancy of RESs generation instability on the capacity of electrochemical accumulators, on the capacity of the system reserve, as well as on the capacity of power lines have been formed. Such dependencies make it possible to more reasonably choose certain methods of redundancy in accordance with the characteristics and requirements of PS.

Battery Sizing for Different Loads and RES Production Scenarios through Unsupervised Clustering Methods

The increasing penetration of Renewable Energy Sources (RESs) in the energy mix is determining an energy scenario characterized by decentralized power production. Between RESs power generation technologies, solar PhotoVoltaic (PV) systems constitute a very promising option, but their production is not programmable due to the intermittent nature of solar energy. The coupling between a PV facility and a Battery Energy Storage System (BESS) allows to achieve a greater flexibility in power generation. However, the design phase of a PV+BESS hybrid plant is challenging due to the large number of possible configurations. The present paper proposes a preliminary procedure aimed at predicting a family of batteries which is suitable to be coupled with a given PV plant configuration. The proposed procedure is applied to new hypothetical plants built to fulfill the energy requirements of a commercial and an industrial load. The energy produced by the PV system is estimated on the basis of a performance analysis carried out on similar real plants. The battery operations are established through two decision-tree-like structures regulating charge and discharge respectively. Finally, an unsupervised clustering is applied to all the possible PV+BESS configurations in order to identify the family of feasible solutions.

A flexible-reliable operation optimization model of the networked energy hubs with distributed generations, energy storage systems and demand response

This paper presents a novel optimization model for the flexible-reliable operation (FRO) of energy hubs (EHs) in electricity, natural gas, and district heating networks. To achieve flexible EH in the presence of renewable energy sources (RESs) and combined heat and power (CHP) system, energy storage systems (ESS) and incentive-based demand response program (IDRP) are used. The proposed problem minimizes the total expected costs of operation, reliability, and flexibility of the energy networks including EHs. The optimization scheme is constrained to the optimal power flow (OPF) equations and the reliability requirements of these networks and the EH model in the presence of sources and active loads, namely ESS and IDRP. Scenario-based stochastic programming (SBSP) is utilized to model uncertainties of load, energy cost, power generation of RES, and network equipment availability. The problem has a mixed-integer nonlinear programming (MINLP) nature. Consequently, a hybrid teaching-learning-based optimization (TLBO) and crow search algorithm (CSA) is used to obtain a reliable optimal solution with a low standard deviation. Finally, by simulating the proposed scheme on a sample test system, the capabilities of this scheme in improving the reliability, operation, and flexibility of energy networks in accordance with the optimal scheduling for EHs are confirmed.

Novel Mode Adaptive Artificial Neural Network for Dynamic Learning: Application in Renewable Energy Sources Power Generation Prediction

A reasonable dataset, which is an essential factor of renewable energy forecasting model development, sometimes is not directly available. Waiting for a substantial amount of training data creates a delay for a model to participate in the electricity market. Also, inappropriate selection of dataset size may lead to inaccurate modeling. Besides, in a multivariate environment, the impact of different variables on the output is often neglected or not adequately addressed. Therefore, in this work, a novel Mode Adaptive Artificial Neural Network (MAANN) algorithm has been proposed using Spearman’s rank-order correlation, Artificial Neural Network (ANN), and population-based algorithms for the dynamic learning of renewable energy sources power generation forecasting model. The proposed algorithm has been trained and compared with three population-based algorithms: Advanced Particle Swarm Optimization (APSO), Jaya Algorithm, and Fine-Tuning Metaheuristic Algorithm (FTMA). Also, the gradient descent algorithm is considered as a base case for comparing with the population-based algorithms. The proposed algorithm has been applied in predicting the power output of a Solar Photovoltaic (PV) and Wind Turbine Energy System (WTES). Using the proposed methodology with FTMA, the error was reduced by 71.261% and 80.514% compared to the conventional fixed-sized dataset gradient descent-based training approach for Solar PV and WTES, respectively.

Modeling and Simulation of Solar Photovoltaic Renewable Energy Sources Power Generation System for MGs and Loss of Mains Detection

Power generation from Renewable Energy Sources (RESs) is steadily increasing due to a number of reasons. These include government subsidies on these RESs, improvement in technology in their manufacture and efforts to reduce greenhouse gas emissions. However, these RESs have associated challenges like intermittency in power generation hence unreliable power supply. Solar PV is gaining popularity as compared to other RESs due to its unique advantages in grid connected applications. It is therefore important to carry out accurate design and enough simulations on its dynamic behavior before actual installation at any given location using local environmental parameters. This paper discusses the detailed modelling, simulation and dynamic behavior of solar PV power generation systems for MG applications using Matlab/Simulink platform.

RETRACTED ARTICLE: An AC–DC/DC–DC hybrid multi-port embedded energy router based steady-state power flow optimizing in power system using substantial transformative energy management strategy

Novel and flexible interconnected architecture for micro grids and their associated energy the management strategy proposes to respond to the increase in renewable energy sources power generation. The key idea of the system is improve the renewable energy generation capacity using a multi-port DC–DC converter, the energy flow between the generation source and the micro grid system can be analyzed with the energy routers. The consumption methods can be used to compensate immediate energy shortage energy production. A multi-port based AC–DC and DC–DC converter and the, power routers are used as energy centers, are improve the stabilize energy flow between micro grids and the main grid. The power generation of the each and every renewable sources are monitored and controlled by the proposed substantial transformative energy management (STEM) strategy also the energy flow variation of the grid system is analyzed with the. This work demonstrates the role of energy routers in optimizing the efficient and flexible way of energy stabilization in grid system. This analysis model establishes the entire system, including power routers, interconnected microcircuits, and the main grid. Interconnected microcircuits were analyzed by various operating systems facilitated by power routers, and corresponding control strategies were developed. The simulation is performed on Mat lab/Simulink simulation platforms, and the results show the effectiveness especially in THD 2.52% is achieved and reliability of the control strategy for micro-grid interconnection and flexible energy flow correspondence.

Coordination of optimal sizing of energy storage systems and production buffer stocks in a net zero energy factory

Green smart factories will be one of the key elements of the future smart grids. They will play an active role in managing the volatility of power generated by renewable energy sources (RES). In order to do that, the green smart factories must set up energy management systems to control the production processes according to both the on-site power generation by RES and external signals sent by the system operators. The aim of the study is to size energy storage systems and production buffer stocks as the flexibility options, allowing the highest integration of power generated by volatile energy within a manufacturing system. The concept of the net zero energy factory is considered. Algorithms for coordinating the flexibility options optimally have been developed. These algorithms are based on mixed-integer linear programming and aim to maximize the matching between RES power generation and flexible demand. A case study is used to verify the framework results. Key performance indices, such as the system self-sufficiency index and the system self-consumption index have been considered for evaluating the solutions developed. Finally, sensitivity analysis has been performed to assess the robustness of the solutions found.

Proactive Resilient Scheduling for Networked Microgrids With Extreme Events

In this paper, we propose a risk-constrained adaptive robust optimization approach to provide proactive resilient scheduling decisions for multiple networked microgrid central controllers under potential extreme events. Our objective is to minimize both risks of false judgement made by microgrid central controllers and damage done to networked microgrids by extreme events through a proactive resilient scheduling process. A risk-constrained adaptive robust optimization approach is developed to handle risks and uncertainties associated with: (i) extreme events that may occur and contingency issues linked to influential buses; (ii) renewable energy sources power generation; (iii) human reactions when faced with an extreme event; and (iv) status of combined cooling, heat and power units. “Budget of uncertainty” and risk-management parameters are utilized together to overcome both overconservative issues of conventional robust optimization and human errors that may occur when making decisions. Extensive simulation results from real-world data sets show that the risk-constrained adaptive robust optimization approach we propose can ensure the resilience of networked microgrids under extreme events.

Optimal battery technology selection and incentive-based demand response program utilization for reliability improvement of an insular microgrid

Improvement of reliability is one of the important issues in the exploitation of microgrid (MG). In this regard, two methods of optimal use of batteries as well as interaction with the consumer in the framework of the use of various types of the demand response programs (DRPs) are the most efficient approaches to improve reliability. This paper presents approaches to select the technology, capacity and optimal rated power of the batteries used in an insular MG, which provides its power through the renewable energy sources (RESs) only. Because of the available uncertainties in power generation of RES, a scenario-based method is used. The purpose of using batteries in this paper is to improve reliability and increase profits from the sales of energy. By applying optimization of the incentive-based DRP in MG with optimization of incentive prices, their effect on the objective functions simultaneously and separately is investigated. The algorithm used for the desired optimization is exchange market algorithm (EMA). The results of optimization indicate the positive effect of the batteries and the incentive-based DRP on the reliability and profitability of the MG.

The “Merit-order effect” of wind and solar power: Volatility and determinants

The investment in renewable energy sources power generation is coming into a stall in face of the removal of incentives in most of the European countries. The technological maturity of wind and solar power generation is the justification basis and market integration of extensive amounts of renewable energy is seen with concern by electricity systems stakeholders. Given the low marginal cost of renewable power generation, the available load in the spot electricity market for conventional and dispatchable power plants is diminishing, the so-called Merit-Order Effect. In this study, we estimate the value of the Merit-Order Effect due to wind and solar power generation in the Iberian electricity market with historical bid supply and demand offers from 2008 to 2017. The estimated values are then used to value determinants and volatility through GARCH econometric modelling. Results demonstrate that the Merit-Order Effect in Iberia is influenced negatively by the residual load and positively by demand, wind power and solar power. Additionally, the high Merit-Order Effect volatility found, mirrors the behavior of spot electricity market.

Stochastic‐based scheduling of the microgrid operation including wind turbines, photovoltaic cells, energy storages and responsive loads

Microgrids with different technologies in distributed generations (DGs), different control facilities and power electronic interfaces require proper management and operation strategies. In these strategies, in order to reach the optimum scheduling, the stochastic nature of some decision variables should be considered. Subsequently, it will lead to a decrease in the forced load curtailment and an increase in the economic efficiency from the perspective of both the maingrid and the microgrid owner. In this study, the availability of dispatchable DGs, energy storage, renewable energy sources (RESs) and the maingrid as well as the power generation of RESs and load are studied through their uncertainty natures. For dealing with these uncertainties, stochastic variables computation module is designed which generates several scenarios by Monte Carlo simulation at each hour. The microgrid operation is optimised in uncertainty environment through a linear two-stage stochastic model. The stochastic scheduling model which is solved by mixed-integer linear programming is compared with a deterministic model through three different cases in presence of demand response on a sample microgrid. The results explicitly show benefits of the proposed stochastic model since it provides accuracy in scheduling and decreases the operation cost.

Modelling of auctioning mechanism for solar photovoltaic capacity

In this work, a modified optimisation model for the integration of renewable energy sources for power-generation (RES-E) technologies in power-generation systems on a unit commitment basis is developed. The purpose of the modified optimisation procedure is to account for RES-E capacity auctions for different solar photovoltaic (PV) capacity electricity prices. The optimisation model developed uses a genetic algorithm (GA) technique for the calculation of the required RES-E levy (or green tax) in the electricity bills. Also, the procedure enables the estimation of the level of the adequate (or eligible) feed-in-tariff to be offered to future RES-E systems, which do not participate in the capacity auctioning procedure. In order to demonstrate the applicability of the optimisation procedure developed the case of PV capacity auctioning for commercial systems is examined. The results indicated that the required green tax, in order to promote the use of RES-E technologies, which is charged to the electricity customers through their electricity bills, is reduced with the reduction in the final auctioning price. This has a significant effect related to the reduction of electricity bills.

Evaluation of Water-Use Needs in the Electricity Generation Sector of Greece

Water conservation needs encourage ‐ among others‐ the investigation of water use in the electricity generation sector. To this end, we currently determine the amounts of water used in thermal electricity generation for both cooling and energy generation purposes, presenting at the same time the respective water needs of renewable energy sources (RES) plants. Furthermore, special attention is given to the country of Greece, for which we evaluate the local power stations’ performance in relation to the local fresh water potential. The study is concluded by realizing that promotion of RES power generation will ensure ‐apart from the satisfaction of other energy and environment‐related targets‐ conservation of valuable water resources in some of the most vulnerable areas of the Greek region.

A hybrid model for the optimum integration of renewable technologies in power generation systems

The main purpose of this work is to assess the unavoidable increase in the cost of electricity of a generation system by the integration of the necessary renewable energy sources for power generation (RES-E) technologies in order for the European Union Member States to achieve their national RES energy target. The optimization model developed uses a genetic algorithm (GA) technique for the calculation of both the additional cost of electricity due to the penetration of RES-E technologies as well as the required RES-E levy in the electricity bills in order to fund this RES-E penetration. Also, the procedure enables the estimation of the optimum feed-in-tariff to be offered to future RES-E systems. Also, the overall cost increase in the electricity sector for the promotion of RES-E technologies, for the period 2010–2020, is analyzed taking into account factors, such as, the fuel avoidance cost, the carbon dioxide emissions avoidance cost, the conventional power system increased operation cost, etc. The overall results indicate that in the case of RES-E investments with internal rate of return (IRR) of 10% the cost of integration is higher, compared to RES-E investments with no profit, (i.e., IRR at 0%) by 0.3–0.5€c/kWh (in real prices), depending on the RES-E penetration level.