Intermittent Nature Of Renewable Energy Sources Research Articles

A frequency stability assessment framework for renewable energy rich power grids

The worldwide power grid is undergoing a paradigm shift due to the rise of renewable energy sources. Traditional rotating synchronous generators are being replaced by inverter-based renewable energy sources (RESs), and this trend is expected to persist in the years to come. As a result, the grid’s inertia is decreasing gradually, which presents significant obstacles to maintaining grid frequency stability. Moreover, due to the uncertainties associated with the generation of RESs and load demand, grid inertia has become a stochastic quantity. In this paper, considering the stochastic characteristics of the power generation, load demand profile and grid inertia, a framework to assess grid frequency stability with high penetration of RESs has been presented. This framework first quantifies grid frequency characteristics at different levels of RES penetration using Monte-Carlo simulation (MCS) considering the intermittent nature of RESs and load demand. This is followed by assessment of frequency stability of the power grid using proposed matrices that quantifies the amount of deviation in grid frequency and rate of change of frequency (RoCoF) outside the boundary prescribed by the grid operator. In addition, the proposed framework evaluates the sensitivity of grid frequency stability to RES penetration levels and quantifies the resiliency against contingency. Rigorous case studies on modified IEEE 39 bus test system have been presented to demonstrate the effectiveness of the proposed framework in assessing grid frequency stability.

Design and Development of a Short-Term Photovoltaic Power Output Forecasting Method Based on Random Forest, Deep Neural Network and LSTM Using Readily Available Weather Features

Renewable energy sources (RES) are an essential part of building a more sustainable future, with higher diversity of clean energy, reduced emissions and less dependence on finite fossil fuels such as coal, oil and natural gas. The advancements in the renewable energy sources domain bring higher hardware efficiency and lower costs, which improves the likelihood of wider RES adoption. However, integrating renewables such as photovoltaic (PV) systems in the current grid is still a major challenge. The main reason is the volatile, intermittent nature of RES, which increases the complexity of the grid management and maintenance. Having access to accurate PV power output forecasting could reduce the number of power supply disruptions, improve the planning of the available and reserve capacities and decrease the management and operational costs. In this context, this paper explores and evaluates three Artificial Intelligence (AI) methods - random forest (RF), deep neural network (DNN) and long short-term memory network (LSTM), which are applied for the task of short-term PV output power forecasting. Following a statistical forecasting approach, the selected models are trained on weather and PV output data collected in Berlin, Germany. The assembled data set contains predominantly broadly accessible weather features, which makes the proposed approach more cost efficient and easily applicable even for geographic locations without access to specialized hardware or hard-to-obtain input features. The performance achieved by two of the selected algorithms indicates that the RF and the DNN models are able to generate accurate solar power forecasts and are also able to handle sudden changes and shifts in the PV power output.

Optimal Energy Management of Virtual Power Plants with Storage Devices Using Teaching-and-Learning-Based Optimization Algorithm

In recent decades, Renewable Energy Sources (RES) have become more attractive due to the depleting fossil fuel resources and environmental issues such as global warming due to emissions from fossil fuel-based power plants. However, the intermittent nature of RES may cause a power imbalance between the generation and the demand. The power imbalance is overcome with the help of Distributed Generators (DG), storage devices, and RES. The aggregation of DGs, storage devices, and controllable loads that form a single virtual entity is called a Virtual Power Plant (VPP). In this article, the optimal scheduling of DGs in a VPP is done to minimize the generation cost. The optimal scheduling of power is done by exchanging the power between the utility grid and the VPP with the help of storage devices based on the bidding price. In this work, the state of charge (SOC) of the batteries is also considered, which is a limiting factor for charging and discharging of the batteries. This improves the lifetime of the batteries and their performance. Energy management of VPP using the teaching-and-learning-based optimization algorithm (TLBO) is proposed to minimize the total operating cost of VPP for 24 hours of the day. The power loss in the VPP is also considered in this work. The proposed methodology is validated for the IEEE 16-bus and IEEE 33-bus test systems for four different cases. The results are compared with other evolutionary algorithms, like Artificial Bee Colony (ABC) algorithm and Ant Lion Optimization (ALO) algorithm.

Coordinating Electric Vehicles and Distributed Energy Sources Constrained by User’s Travel Commitment

Distributed energy sources including renewable energy (RE) sources and electric vehicle (EV) discharging offer opportunities to improve grid performance. Although uncoordinated EV charging can perfectly meet users’ driving needs, it brings great challenges in maintaining the quality of low voltage (LV) distribution grids. Most existing EV control strategies do not take into account the heterogeneous nature of EV charging, including the various users’ travel needs and the intermittent nature of RE sources. This article proposes a hierarchical control method that simultaneously coordinates EV charging by considering a dynamic energy tariff, RE sources, EV hardware characteristics, and particularly the EV’s driving needs. The proposed local controller sends the EV’s charging priority and required energy to the central controller based on the EV’s hardware characteristics and users’ driving needs. The central controller then uses this information along with the energy tariff from the retailer and the present grid performance to control EV’s charging or discharging power. The efficacy of this hierarchical control method is evaluated using an Australian LV grid. The obtained results show that this method can reduce the neutral current and voltage imbalance with the maximized usage of renewable energy resources while the EV users’ driving needs are met.

Cooperative energy transactions in micro and utility grids integrating energy storage systems

Over the past few years, Micro Grids (MGs) have gained much popularity due to two-way communication in the power network with special emphasis on Distributed Energy Resources (DERs), which comprise of both Renewable Energy Sources (RESs) and Nonrenewable Energy Sources (NRESs). Currently, the main focus of researchers is to deal with the intermittent nature of RESs, which lead to the fluctuations in power production and dispatch. In this paper, direct energy trading among MGs is considered as an assuring solution for improving the grid stability, reducing power line losses and minimizing energy trading cost. The focus of this work is on energy transactions amongst multiple MGs within the same geographic region. In the proposed method, coalitions among MGs are made on the basis of the distance between them for energy transaction. Furthermore, an Energy Transaction Algorithm (ETA) is proposed for energy trading among MGs in the coalitions. Energy Storage System (ESS) is also integrated, which stores energy when an MG has surplus energy and utilizes the stored energy if it becomes energy deficient. Simulations are performed and the results demonstrate that energy transactions among MGs in the proposed method minimize the power line losses and energy trading cost up to 34.6% and 14%, respectively as compared to the existing method.

Optimal techno-economic design of hybrid PV/wind system comprising battery energy storage: Case study for a remote area

Due to the sustainability and emission-free property of hybrid renewable energy sources (RESs), they became challenging alternative sources to conventional energy production facilities. However, cost of energy (COE) and the intermittent nature of RESs are two adverse factors complicating the problem of system appropriate sizing. This study develops a generalized mathematical model to find the optimal PV/wind/battery system sizes for remote areas. The proposed methodology is applied on a remote area in Egypt-Sinai called Ras-Shaitan. The purpose of the optimization process is to meet the load demand while minimizing the COE under different loss of power supply probability (LPSP). The grey wolf optimizer (GWO) is employed to decide the number of units among photovoltaic, wind and battery banks to achieve the best minimum objective value. To validate the solution quality generated by the GWO, the widely used HOMER software, the particle swarm optimizer (PSO) and genetic algorithm (GA) as a well-known meta-heuristic algorithms, are applied to the same and their results are compared with those obtained by the GWO. Further validations are made, in which, a new algorithm called wild horse optimizer (WHO) is also applied as a new algorithm and its results compared to aforementioned methods. Moreover, different solutions are offered according to the LPSP. The optimal system offered by HOMER has COE of 0.118 $/kWh, while GWO resulted in a save of about 17% of the COE. The GWO has a better convergence trend and best statistics measures than the PSO.

Automatic Load Frequency Control in an Isolated Micro-grid with Superconducting Magnetic Energy Storage Unit using Integral Controller

Due to depletion of fossil fuels and increasing power demand, employing Renewable Energy Sources (RES) in the form of micro-grid has become very essential. However, the reliable operation and control relies on the intermittent nature of RES and maintaining the frequency within the acceptable limit is the challenging task in isolated micro-grid. This paper presents the Automatic Load Frequency Control (ALFC) in an isolated Micro-Grid (MG) with Superconducting Magnetic Energy Storage (SMES) unit using Integral controller, to enhance the stability of the system frequency. The simulation result shows that the dynamic performance of the proposed system is significantly improved while incorporating the SMES Unit. The simulations are carried out in MATLAB – Simulink.

Wind and Solar Intermittency and the Associated Integration Challenges: A Comprehensive Review Including the Status in the Belgian Power System

Renewable Energy Sources (RES) have drawn significant attention in the past years to make the transition towards low carbon emissions. On the one hand, the intermittent nature of RES, resulting in variable power generation, hinders their high-level penetration in the power system. On the other hand, RES can aid not only to supply much more eco-friendly energy but also it allows the power system to enhance its stability by ancillary service provision. This article reviews the challenges related to the most intermittent RES utilised in Belgium, that is, wind energy and solar energy. Additionally, wind speed and solar irradiance variations, which are the cause of wind and solar intermittency, are studied. Then, recent techniques to forecast their changes, and approaches to accommodate or mitigate their impacts on the power system, are discussed. Finally, the latest statistics and future situation of RES in the Belgian power system are evaluated.

A Cooperative Energy Management in a Virtual Energy Hub of an Electric Transportation System Powered by PV Generation and Energy Storage

Electric transport systems and renewable energy sources (RESs) have recently attracted significant interest, because of the limitations and drawbacks of fossil fuels and the growing demand for utilization of clean energy. Suitable paradigms are needed to manage the atypical and heterogeneous load of electric vehicles (EVs), the intermittent nature of RESs, and the changeability of electrical loads in power networks. The technical and commercial operation of an integrated system comprising an electric transportation system with a battery-powered bus [electric bus (eBus)] charging station and an EV parking lot, integrated with solar photovoltaic (PV) generation, and combined with a battery storage system (BSS), as a virtual energy hub (VEH) is proposed. Moreover, a cooperative decision-making (CDM) strategy is proposed for the VEH, where the active and reactive power flows and the economic operation of the VEH are scheduled using a novel three-stage cooperative control system. A supervisory control system is developed using agents to realize the scheduled CDM of the VEH and to assign the control parameters for the provision of ancillary services. The VEH agent can operate autonomously during the real-time operation to cope with the volatility of the power system based on the optimal predetermined set points from the grid agent. The effectiveness of the CDM methodology is studied and evaluated through different realistic scenarios. The results show that the proposed CDM can organize the effective energy management (EM) with a minimum operational cost to the VEH. Furthermore, because of the designed supervisory control and the provision of ancillary services using the VEH, the grid requirements can be effectively managed.

Influences of vehicle to grid (V2G) on power grid: An analysis by considering associated stochastic parameters explicitly

Integration of the electric vehicles (EVs) and vehicle-to-grid (V2G) technology with renewable energy sources (RESs) is the hottest topics among researchers. Many studies have been done in this field, but in most of them, discussion of aspects of electrified transportation have been limited. The V2G system due to its structure and supporting role for the grid influences the power demand and supply curve simultaneously depending on the patterns of driving, preferences for charging/discharging times, location, and the infrastructure of the system. Due to complexity of this system, it is necessary to cover all the influential elements in one package and analyze the impact of the system on them in a unique process. In this paper, we apply a precise and efficient methodology to analyze the impacts of the EVs and V2G on reliability, cost, and emissions with respect to the power grid. The strength of this paper is the applicability of the suggested methodology to the power grids with different patterns and features with respect to shares of RESs generated power by explicitly considering the stochastic parameters shaping the daily demand/supply curves. We introduce two novel indexes for measuring the reliability of the power grid based on availability of the RESs under power supply systems: stochastic and constant power supply. To cover all the possibilities, based on different penetration levels of EVs and V2G, charging modes, locations and schedules several cases are defined and for each case, the Monte Carlo simulation is used to evaluate the impacts of the considered cases regarding the reliability, emission, and cost of the power grid. In the numerical analysis under assumed cases, results show integration of the EVs and V2G system in stochastic power supply (high RESs) improves the power grid’s efficiency in terms of decreasing the total cost and emission rate. The intermittent nature of RESs can increase fluctuation in the power grid and consequently negatively affect the reliability of the system but our analysis indicates that integration of these sources with V2G system can improve the reliability of the power grid.

A Review on Primary and Secondary Controls of Inverter-interfaced Microgrid

The increasing demand of electricity and environmental issues associated with conventional power generationplants are increasing significantly. Modern technology has transformed the conventional power system through the integrationof distributed generation (DG). The new power systemequipped with cutting edge technology encourages the clean energy employing most DG units based on renewable energysources (RES) and this forms Microgrid (MG), which operateseither on grid-connected mode or islanded mode. The intermittent nature of RES and varying load pose substantial obstacles, i.e. voltage and frequency instability, the unreliability of RES. Unequal feeder impedances, non-linear loads are considered aspresent challenges in MG control. Hierarchical control has beenuseful in undertaking solutions to these issues. This paper covers the depth insight of different control methods applied at theprimary and secondary control level in hierarchical control. Inprimary control, the classification based on droop and non-droop control has been discussed. The virtual synchronizationmachine (VSM) based control method of control has been reviewed. Voltage and frequency restoration control and economical operations at decentralized and centralized secondary control have been analyzed in detail. Based on the understandingdeveloped from literature, critical discussion on microgrid control and future trends have been discussed at the end of the paper to provide future research perspectives.

An Optimal Solution for Eco-reliability Stochastic Modeling of Simultaneous Reconfiguration of Distribution Networks and Allocation of Renewable Energy Resources, Capacitors and D-facts

Objective: A new method based on stochastic modeling of simultaneous optimal reconfiguration of distribution networks and allocation of multiple Distribution STATic COMpensator and renewable energy resources as new developed technologies is suggested in this paper. Background: This problem involves vital uncertain parameters such as: intermittent nature of renewable energy sources, load forecasting and market price errors. Krill Herd Algorithm as a powerful optimization solution is applied to solve suggested problem. Methods: Several effective cost based consequences can be achieved by proposed technique like: DG units installation and operation costs, D-Statcom installation cost, capacitor costs, cost of purchased energy from the transmission network, active and reactive power losses cost, line upgrade cost and distribution system reconfiguration cost. Results: In addition, energy not supplied as a vital reliability index incorporated in the cost function aims to improve network’s reliability efficiently. Conclusion: IEEE 33 bus radial distribution system is used here to check the practicability of the proposed scheme.

Multi-objective energy storage capacity optimisation considering Microgrid generation uncertainties

High penetration of renewable energy sources (RES) in distributed Microgrid (MG) systems provides significant economic and environmental benefits. Nevertheless, the intermittent nature of RESs is the greatest challenge in the efficient utilisation of these resources. Using Battery Energy Storage Systems (BESS) in MGs is proposed as an effective solution to mitigate the fluctuations of RESs power generation. However, BESSs are expensive components with a short lifetime and therefore, they should be optimally sized according to the characteristics and requirements of MGs. This paper proposes a BESS sizing approach to determine the capacity and power rating of the BESS in a Grid-Connected MG, taking economic and technical criteria as well as the uncertainty of dispatchable generators (DG) and RESs into consideration. The technical requirements include the degree of reliability, BESS degradation, and operating constraints of the DGs. On the other hand, the cost factors associated with the utility grid including peak cost, valley cost, and Time of Use tariff (TOU) are taken into account. In this paper, a multi-objective mixed-integer quadratic model is developed to include the aforementioned parameters in the BESS capacity optimisation algorithm. Inspecting the impact of the considered parameters on the optimal size of the BESS and total cost of the MG, four case scenarios are studied. The first two scenarios evaluate the effect of the BESS depreciation, whereas the third and fourth scenarios investigate the impact of grid volatility (GV) and reliability indices on the optimal solution. The obtained results from the case studies verify the effectiveness of the proposed BESS sizing method.

Impact of Smart Restoration and Energy Storage Systems on the Reliability of Electric Microgrid

In this paper, the impact of smart restoration and the integration of energy storage units on the reliability of the wind and solar-based microgrid (MG) are investigated. Since the early development and utilization of renewable energy sources (RES), especially wind and solar, they are being given increasing consideration owing to many reasons, such as the enhanced public awareness and the growing concerns regarding the potential impact of conventional energy systems on the environment. However, the random and intermittent nature of RES can affect their reliability in electrical networks. In the twenty-first century, the concept of a smart grid has become a major research area that addresses the challenges of the future power systems. One of the main characteristics of smart grids is the smart self-healing. The main objective of self-healing control is to monitor the real-time network operation, predict the power grid state, and detect and isolate the faults in the system without human intervention. We focus at smart restoration and investigate the integration of energy storage units on the reliability of wind- and solar-based MG. Two models are examined to reduce the effects of uncertainty in wind- and solar-generated power: the ARMA model for wind speed and the triple exponential smoothing model for solar energy prediction. This study was conducted on a Roy Billinton Test System.

Transformation of microgrid to virtual power plant – a comprehensive review

To provide continuity of balancing demand and generation, renewable sources will be more active than today in near future due to the tendency of massive investment on renewable energy sources (RESs) by countries. However, due to the uncertain and intermittent nature of RESs, RESs would create problems on power system operations such as power quality, efficiency, stability and reliability. Owing to having problems with RESs integration, virtual power plant (VPP) has introduced to make this integration smooth without compromising the grid stability and reliability along with offering many other techno-economic benefits. This study reviews structures, types, architecture and operations of VPP along with the status of present implementations worldwide. The types of VPP are introduced in details with the optimisation algorithm used with each type. In addition, VPP is linked with the most of the components in power systems such as distributed generation, active prosumers, transmission system operator and distribution system operator, grid services such as fault ride through, reactive power control as well as with the help of technology such as communications, control and optimisations. This study gives a comprehensive outline of transforming microgrid to VPP that is useful for researchers, consumers, prosumers and utility operators.

Enhancing the Optimization of Hybrid Renewable Energy Systems by Using Statistical Calculations and Data Mining Analysis

According to numbers and charts reported recently in the global renewable status and the international energy agency, Renewable energy sources (RES) surpassed the natural gas and occupied the second position as a source of electricity. However, the intermittent nature of RES, causes a big problem of trifold dimensions: System cost, environmental impacts, and system reliability. Studies found that, using diverse RES combined in hybrid renewable energy systems (HRES) can handle these issues efficiently but only when utilizing good optimization techniques. Consequently, this paper derives the most suitable optimization techniques of HRES in two steps. Firstly, it conducts a comprehensive review of hundreds of the most recent research papers on HERS. Secondly , it runs statistical calculations and data mining analysis on the big data collected in the first step. The outcomes of these calculations and analyses are represented in form of statistical results and frequent patterns rulesets. Results proved that the statistical results go a long with the rulesets and they give typical results. Thereby, they validated each other and verified the correctness of the overall results. As a result, the paper produces two great databases. The first one can be considered as a guide for researchers to determine the best techniques for optimizing HERS systems. The second database of statistical results and frequent pattern rulesets can help researchers immensely in predicting the most suitable optimization techniques and algorithms for designing and developing HERS systems.Â

Robust optimisation for deciding on real‐time flexibility of storage‐integrated photovoltaic units controlled by intelligent software agents

The increasing penetration of Renewable Energy Sources (RES), the liberalization of the electricity markets across the world and devices such as smart meters present the end-users of the power system with new opportunities to decrease their electricity costs or become active electricity market participants. However, the intermittent nature of RES and dynamic electricity prices require tools against uncertainty to protect the end-users from underutilizing their assets. In this work, we examine the effectiveness of Robust Optimization (RO) in maximizing the economic benefit of the owner of a home battery storage system in the presence of uncertainty in dynamic electricity prices. The advantage of the robust model is that it keeps its linear class, thus it is not too computationally intensive to be included in the control algorithm of a residential energy storage controller. In the use-case, the aggregating entity makes requests for flexibility and coordinates 100 such devices using a price signal, while the storage controller is doing dynamic electricity price arbitrage. The results indicated that the RO approach can be beneficial for a non-conservative agent in the case of low daily price fluctuations, while, in summer, when the price fluctuations are higher, uncertainty can be ignored.

Enabling Privacy in Vehicle-to-Grid Interactions for Battery Recharging

The diffusion of Electric Vehicles (EV) fostered by the evolution of the power system towards the new concept of Smart Grid introduces several technological challenges related to the synergy among electricity-propelled vehicle fleets and the energy grid ecosystem. EVs promise to reduce carbon emissions by exploiting Renewable Energy Sources (RESes) for battery recharge, and could potentially serve as storage bank to flatten the fluctuations of power generation caused by the intermittent nature of RESes by relying on a load aggregator, which intelligently schedules the battery charge/discharge of a fleet of vehicles according to the users’ requests and grid’s needs. However, the introduction of such vehicle-to-grid (V2G) infrastructure rises also privacy concerns: plugging the vehicles in the recharging infrastructures may expose private information regarding the user’s locations and travelling habits. Therefore, this paper proposes a privacy-preserving V2G infrastructure which does not disclose to the aggregator the current battery charge level, the amount of refilled energy, nor the time periods in which the vehicles are actually plugged in. The communication protocol relies on the Shamir Secret Sharing threshold cryptosystem. We evaluate the security properties of our solution and compare its performance to the optimal scheduling achievable by means of an Integer Linear Program (ILP) aimed at maximizing the ratio of the amount of charged/discharged energy to/from the EV’s batteries to the grid power availability/request. This way, we quantify the reduction in the effectiveness of the scheduling strategy due to the preservation of data privacy.