•Multi-objective optimal sizing of HRES is implemented considering resource uncertainties to obtain more realistic results.•A novel method in using CCP is proposed to estimate the expected value of ...the objective function affected by uncertain values.•Proposed method reduces the evaluation time of the design candidate and consequently the run time of the NSGA-II program.
The optimum design of Hybrid Renewable Energy Systems (HRES) depends on different economical, environmental and performance related criteria which are often conflicting objectives. The Non-dominated Sorting Genetic Algorithm (NSGA-II) provides a decision support mechanism in solving multi-objective problems and providing a set of non-dominated solutions where finding an absolute optimum solution is not possible. The present study uses NSGA-II algorithm in the design of a standalone HRES comprising wind turbine, PV panel and battery bank with the (economic) objective of minimum system total cost and (performance) objective of maximum reliability. To address the uncertainties in renewable resources (wind speed and solar irradiance), an innovative method is proposed which is based on Chance Constrained Programming (CCP). A case study is used to validate the proposed method, where the results obtained are compared with the conventional method of incorporating uncertainties using Monte Carlo simulation.
The inappropriate mechanism designs for demand response (DR) in the community of microgirds (CoMGs) may cause massive problems, such as increase of consumers' costs, rebound peaks, and thereby lack ...of optimality in the network. In this article, a bilevel energy management system (EMS) is proposed to tackle the challenges associated with DR programs for CoMGs. The current structure successfully models users' behavior and dissatisfaction in the first level of optimization to develop best DR program for each of them. Moreover, in the second level, power system constraints are taken into account to prevent voltage and current deviation from their statutory limits. Each user is assumed to be part of a microgrid (MG) whose operation is controlled and optimized through its local EMS in the first level. On the other hand, the overall operation of all MGs is delegated to the whole system operator, which acts as the central EMS (CEMS) in the second level. An iterative transactive energy management method is proposed by CEMS to fairly limit the excess power of the MGs one day ahead for voltage and current regulation. The obtained results indicate the effectiveness of the proposed structure in preventing discomfort issues, voltage deviation and creation of the rebound peaks in the system.
The continuous deployment of distributed energy sources and the increase in the adoption of electric vehicles (EVs) require smart charging algorithms. The existing EV chargers offer limited ...flexibility and controllability and do not fully consider factors (such as EV user waiting time and the length of next trip) as well as the potential opportunities and financial benefits from using EVs to support the grid, charge from renewable energy, and deal with the negative impacts of intermittent renewable generation. The lack of adequate smart EV charging may result in high battery degradation, violation of grid control statutory limits, high greenhouse emissions, and high charging cost. In this article, a neuro-fuzzy particle swarm optimization (PSO)-based novel and advanced smart charge controller is proposed, which considers user requirements, energy tariff, grid condition (e.g., voltage or frequency), renewable (photovoltaic) output, and battery state of health. A rule-based fuzzy controller becomes complex as the number of inputs to the controller increases. In addition, it becomes difficult to achieve an optimum operation due to the conflicting nature of control requirements. To optimize the controller response, the PSO technique is proposed to provide a global optimum solution based on a predefined cost function, and to address the implementation complexity, PSO is combined with a neural network. The proposed neuro-fuzzy PSO control algorithm meets EV user requirements, works within technical constraints, and is simple to implement in real time (and requires less processing time). Simulation using MATLAB and experimental results using a dSPACE digital real-time emulator are presented to demonstrate the effectiveness of the proposed controller.
Performance of a HRES (hybrid renewable energy system) is highly affected by changes in renewable resources and therefore interruptions of electricity supply may happen in such systems. In this ...paper, a method to determine the optimal size of HRES components is proposed, considering uncertainties in renewable resources. The method is based on CCP (chance-constrained programming) to handle the uncertainties in power produced by renewable resources. The design variables are wind turbine rotor swept area, PV (photovoltaic) panel area and number of batteries. The common approach in solving problems with CCP is based on assuming the uncertainties to follow Gaussian distribution. The analysis presented in this paper shows that this assumption may result in a conservative solution rather than an optimum. The analysis is based on comparing the results of the common approach with those obtained by using the proposed method. The performance of the proposed method in design of HRES is validated by using the Monte Carlo simulation approach. To obtain accurate results in Monte Carlo simulation, the wind speed and solar irradiance variations are modelled with known distributions as well as using time series analysis; and the best fit models are selected as the random generators in Monte Carlo simulation.
•Solving chance constrained problems with assumption of normal Gaussian distribution ignores a set of feasible solutions.•Proposed method solves chance constrained problem with no assumption on the type of joint distribution of uncertainties.•Depending on the site location different modelling methods should be used for wind speed and solar irradiance variations.
One of the main reasons attributed to the slow uptake of grid-connected residential PV (photovoltaic) systems, is the lack of information about the near-term economic benefits which are as important ...as long-term viability for residential customers. This paper presents a comparative assessment of the near-term economic benefits of grid-connected residential PV systems. Case studies from the UK and India are taken as examples, as they vary significantly in solar resource, customer demands, electricity prices and financial support mechanisms. A metric termed PEUC (prosumer electricity unit cost) is proposed to develop an economic evaluation methodology to assess the near-term benefits from PV systems. The results obtained showed that, under the present financial support mechanisms, domestic PV systems provide near-term economic benefits in most locations in India. For most locations in the UK, cost reduction is needed to achieve near-term financial benefits and this varies depending on the location of installation. The results presented demonstrate the importance of location specific system planning and demand-generation matching through optimal sizing of the PV system and demand side management.
•Economic assessment based on a proposed “Prosumer Electricity Unit Cost”.•Analysis of the potential of residential GCPV (grid-connected PV) systems.•Techno-economic comparison of residential GCPV systems between the UK and India.•Analysis of the near-term economic benefits of residential GCPV systems.
Wind energy harvesting for electricity generation has a significant role in overcoming the challenges involved with climate change and the energy resource implications involved with population growth ...and political unrest. Indeed, there has been significant growth in wind energy capacity worldwide with turbine capacity growing significantly over the last two decades. This confidence is echoed in the wind power market and global wind energy statistics. However, wind energy capture and utilisation has always been challenging. Appreciation of the wind as a resource makes for difficulties in modelling and the sensitivities of how the wind resource maps to energy production results in an energy harvesting opportunity. An opportunity that is dependent on different system parameters, namely the wind as a resource, technology and system synergies in realizing an optimal wind energy harvest. This paper presents a thorough review of the state of the art concerning the realization of optimal wind energy harvesting and utilisation. The wind energy resource and, more specifically, the influence of wind speed and wind energy resource forecasting are considered in conjunction with technological considerations and how system optimization can realise more effective operational efficiencies. Moreover, non-technological issues affecting wind energy harvesting are also considered. These include standards and regulatory implications with higher levels of grid integration and higher system non-synchronous penetration (SNSP). The review concludes that hybrid forecasting techniques enable a more accurate and predictable resource appreciation and that a hybrid power system that employs a multi-objective optimization approach is most suitable in achieving an optimal configuration for maximum energy harvesting.
Electro-mobility has become an increasingly important research problem in urban cities. Due to the limited electricity of battery, electric vehicle (EV) drivers may experience discomfort for long ...charging waiting time. Different from plug-in charging technology, we investigate the battery switch technology to improve EV drivers' comfort (e.g., reduce the service waiting time from tens of minutes to a few minutes), by benefiting from switchable (fully recharged) batteries cycled at charging stations (CSs). Since demand hotspot may still happen at CSs (e.g., running out of switchable batteries), incoming EVs may need to wait for additional time to get their battery switched, and thus, the EV drivers' comfort is degraded. First, we propose a centralized reservation-enabling service, considering EVs' reservations (including arrival time, expected charging time of their batteries to be depleted) to optimally coordinate their battery switch plans. Second, a decentralized system is further proposed, by facilitating the vehicle-to-vehicle anycasting to deliver EV's reservations. This helps to address some of the privacy issues that can be materialized in a centralized system and reduce communication cost (e.g., through cellular network for reservation making). Results under the Helsinki city scenario show a tradeoff between comparable performance (e.g., service waiting time, number of switched batteries) and cellular network cost for EVs' reservations delivery.
•Synthetic electrical load profiles generation for power networks is proposed.•ANN training using publicly available weather and electrical demand data.•ANN learning from limited datasets is improved ...by using Bayesian regularization.•ANN prediction extrapolation by incorporation of domain knowledge is proposed.•Analysis presented shows close agreement with actual real load profiles.
Electrical load profiles of a particular region are usually required in order to study the performance of renewable energy technologies and the impact of different operational strategies on the power grid. Load profiles are generally constructed based on measurements and load research surveys which are capital and labour-intensive. In the absence of true load profiles, synthetically generated load profiles can be a viable alternative to be used as benchmarks for research or renewable energy investment planning. In this paper, the feasibility of using publicly available load and weather data to generate synthetic load profiles is investigated. An artificial neural network (ANN) based method is proposed to synthesize load profiles for a target region using its typical meteorological year 2 (TMY2) weather data as the input. To achieve this, the proposed ANN models are first trained using TMY2 weather data and load profile data of neighbouring regions as the input and targeted output. The limited number of data points in the load profile dataset and the consequent averaging of TMY2 weather data to match its period resulted in limited data availability for training. This challenge was tackled by incorporating generalization using Bayesian regularization into training. The other major challenge was facilitating ANN extrapolation and this was accomplished by the incorporation of domain knowledge into the input weather data for training. The performance of the proposed technique has been evaluated by simulation studies and tested on three real datasets. Results indicate that the generated synthetic load profiles closely resemble the real ones and therefore can be used as benchmarks.
Incentives, such as the Feed-in-tariff are expected to lead to continuous increase in the deployment of Small Scale Embedded Generation (SSEG) in the distribution network. Self-Excited Induction ...Generators (SEIG) represent a significant segment of potential SSEG. The quality of SEIG output voltage magnitude and frequency is investigated in this paper to support the SEIG operation for different network operating conditions. The dynamic behaviour of the SEIG resulting from disconnection, reconnection from/to the grid and potential operation in islanding mode is studied in detail. The local load and reactive power supply are the key factors that determine the SEIG performance, as they have significant influence on the voltage and frequency change after disconnection from the grid. Hence, the aim of this work is to identify the optimum combination of the reactive power supply (essential for self excitation of the SEIG) and the active load (essential for balancing power generation and demand). This is required in order to support the SEIG operation after disconnection from the grid, during islanding and reconnection to the grid. The results show that the generator voltage and speed (frequency) can be controlled and maintained within the statuary limits. This will enable safe disconnection and reconnection of the SEIG from/to the grid and makes it easier to operate in islanding mode.
This paper describes a novel approach in developing a model for forecasting of global insolation on a horizontal plane. In the proposed forecasting model, constraints, such as latitude and whole ...precipitable water content in vertical column of that location, are used. These parameters can be easily measurable with a global positioning system (GPS). The earlier model was developed by using the above datasets generated from different locations in India. The model has been verified by calculating theoretical global insolation for different sites covering east, west, north, south and the central region with the measured values from the same locations. The model has also been validated on a region, from which data was not used during the development of the model. In the model, clearness index coefficients (KT) are updated using the ensemble Kalman filter (EnKF) algorithm. The forecasting efficacies using the KT model and EnKF algorithm have also been verified by comparing two popular algorithms, namely the recursive least square (RLS) and Kalman filter (KF) algorithms. The minimum mean absolute percentage error (MAPE), mean square error (MSE) and correlation coefficient (R) value obtained in global solar insolation estimations using EnKF in one of the locations are 2.4%, 0.0285 and 0.9866 respectively.
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