Renewable-based Distributed Generation Research Articles

Stochastic Planning for Optimal Allocation of Fast Charging Stations and Wind-Based DGs

To unlock the green potential of electric vehicles (EVs), renewable energy sources are necessary for forming a decarbonized grid. With the rising demand for EVs and fast-charging stations (FCSs), the installation of renewable-based distributed generators (DGs) can further defer network upgrades and minimize energy losses. In this article, a new planning model is proposed to site and size FCSs and wind-based DGs, taking into account the stochastic nature of FCSs, residential EV loads, and renewable generation. The proposed planning model considers different cost and revenue components associated with FCSs and wind-based DGs. A typical distribution network is overlaid on a geographic map to illustrate how the network is connected with the municipal layout, thus taking into account practical constraints for installing FCSs and DGs in the vicinity of each bus. A novel scoring scheme that considers practical geographical aspects is developed to quantify the attractiveness of FCS candidate locations to the drivers of EVs. This scheme is essential to ensure installing FCSs at locations that maximize revenue to investors. Results demonstrate that planning for both FCSs and wind-based DGs is effective from a financial standpoint.

Scenario-Based Stochastic Framework for Optimal Planning of Distribution Systems Including Renewable-Based DG Units

Renewable energy-based distributed generators are widely embedded into distribution systems for several economical, technical, and environmental tasks. The main concern related to the renewable-based distributed generators, especially photovoltaic and wind turbine generators, is the continuous variations in their output powers due to variations in solar irradiance and wind speed, which leads to uncertainties in the power system. Therefore, the uncertainties of these resources should be considered for feasible planning. The main innovation of this paper is that it proposes an efficient stochastic framework for the optimal planning of distribution systems with optimal inclusion of renewable-based distributed generators, considering the uncertainties of load demands and the output powers of the distributed generators. The proposed stochastic framework depends upon the scenario-based method for modeling the uncertainties in distribution systems. In this framework, a multi-objective function is considered for optimal planning, including minimization of the expected total power loss, the total system voltage deviation, the total cost, and the total emissions, in addition to enhancing the expected total voltage stability. A novel efficient technique known as the Equilibrium Optimizer (EO) is actualized to appoint the ratings and locations of renewable-based distributed generators. The effectiveness of the proposed strategy is applied on an IEEE 69-bus network and a 94-bus practical distribution system situated in Portugal. The simulations verify the feasibility of the framework for optimal power planning. Additionally, the results show that the optimal integration of the photovoltaic and wind turbine generators using the proposed method leads to a reduction in the expected power losses, voltage deviations, cost, and emission rate and enhances the voltage stability by 60.95%, 37.09%, 2.91%, 70.66%, and 48.73%, respectively, in the 69-bus system, while in the 94-bus system these values are enhanced to be 48.38%, 39.73%, 57.06%, 76.42%, and 11.99%, respectively.

Advanced Relaying for DG-Penetrated Distribution System

The proliferation of renewable-based distributed generators poses protection challenges for the overcurrent relays due to the intermittent nature and control schemes. Converters present in the AC side restrict the fault current up to two per unit. Therefore, the overcurrent relay present in the AC microgrid maloperates during the fault. The proposed technique is based on the corresponding sum of the current samples obtained after every half-cycle (Ts/2), computed for each phase, and utilized to derive an index to detect the fault. The index thus derived is robust to noises, load variations, and other transients in the system. However, the technique requires removing transients and other noises before comparing the threshold for fault detection, which is resolved using the fast Fourier transform (FFT). Further, the magnitude of decaying DC (as another index) present in the signal is calculated using the least square technique for detecting the three-phase fault. The proposed algorithm is tested on an IEEE 14-bus system and a Real-Time Digital Simulator microgrid model. The fault detection technique is accurate for different faults under different operating conditions like noise, CT saturation, sudden load change, etc.

Optimal stochastic scenario-based allocation of smart grids’ renewable and non-renewable distributed generation units and protective devices

The smart grid reliability is dramatically affected due to system uncertainties. Although much efforts have been devoted to developing the Monte Carlo simulation (MCS)-based or analytical methods for reliability-based optimal allocation distributed generation (DGs) and protective devices (PDs), there is a research gap about developing the probabilistic scenario-based optimization methods. This paper proposes a novel stochastic scenario-based reliability evaluation method for optimal allocation of smart grids’ PDs and DGs. The scenario reduction is applied using the k-means algorithm and modified system state, including the clusters of renewable-based DGs. The malfunction of PDs is concerned, which is one of the most important contributions of the introduced method. The introduced clustering-based reliability evaluation method is applied to IEEE 33-bus test system. Test results infer that around 10% inaccuracy occurs in deterministic approaches without considering uncertainties of DGs and PDs. Obtained test results also imply that the impacts of renewable DGs’ uncertainties are more considerable than eventual malfunctions of PDs. The MCS-based methods are used to verify the precision of the introduced method. Moreover, by comparing the introduced method with other available analytical methods, it is shown that the obtained results are 1.2% more precise than current analytical ones.

Hosting capacity enhancement of renewable-based distributed generation in harmonically polluted distribution systems using passive harmonic filtering

In spite of being economically viable for numerous applications, renewable energy cannot realise its true penetration potential until some of the barriers are not tackled with excellence. Such barriers are harmonic distortion, distribution cable’s ampacity and voltage rise limits, those put bound on the maximum allowable penetration level of renewable energy. This paper formulates the enhancement of power quality-constrained hosting capacity (HC) as a multi-objective optimization (MOO) problem under several constraints of system’s performance indices (PIs). The considered performance indices are individual order and total harmonic distortion in the line current and point of common coupling (PCC)’s voltage, load power factor (PF), distribution line’s ampacity and steady-state voltage profile. In the formulated multi-objective optimization model, an optimal design of a third-order damped filter and size of the distributed generation (DG) unit is simultaneously determined for achieving maximum hosting capacity and power factor at the PCC while keeping system’s other indices such as total voltage harmonic distortion (TVHD) and total filter cost (FC) incurred at a minimum by obtaining a best-compromised solution using the newly proposed Pareto-based multi-objective firefly algorithm (MOFA). The extension of the multi-objective firefly algorithm is considered for producing the Pareto optimal front and various conclusions are drawn by analysing the trade-offs among the objectives by plotting the same on different 2-axis planes. The optimization efficiency and superiority of the proposed multi-objective firefly algorithm based hosting capacity enhancement approach is validated by comparing the results with those obtained by popular multi-objective PSO (MOPSO) and non-dominated sorting genetic algorithm (NSGA-II) under similar objectives. Also, the results of the proposed methodology are compared with those achieved from one of the most recently introduced hosting capacity enhancement approaches in literature. Eventually, the impacts of different background voltage distortion (BVD) levels and load-side’s nonlinearity levels (NLLs) on filter performance, particularly filter cost as well as enhanced hosting capacity, are analysed and various conclusions are drawn.

Simultaneous Distributed Generation and Electric Vehicles Hosting Capacity Assessment in Electric Distribution Systems

This work presents a strategy, from the perspective of the distribution system operator (DSO), that aims at simultaneously estimating the maximum penetration levels of renewable-based distributed generation (DG) and electric vehicles (EVs) that can be accommodated into an electric distribution system (EDS). To estimate such capacity, operational resources such as generation curtailment and controllable features of EVs can be managed to ensure the safe operation of the EDS and avoid infeasible operational conditions. Through a multi-period representation, the proposed strategy models the variability in demand consumption and DG power production. In addition, driving patterns of EV owners and energy requirements of EVs, obtained through probability density functions, are incorporated in this representation. Inherently, the problem is represented as an optimization model, and to determine its solution, an algorithm based on the metaheuristics greedy randomized adaptive search and tabu search (GRASP-TS) is developed. The applicability of the planning strategy is assessed on a 33-bus EDS under different test conditions and the numerical results show that higher penetrations of EVs and renewable-based DG can be accommodated without impacting the safe operation of the EDS. The results also demonstrate that by controlling the power draw by EV aggregators, an increase of 9% can be obtained in the DG installed capacity compared to the case of uncontrolled charging of EVs. In addition, the scalability of the proposed approach is studied using two distribution systems, the 83-bus system and the 135-bus system, where the results show that the convergence of the algorithm is achieved in a few iterations.

Linear network model for integrated power and gas distribution systems with bidirectional energy conversion

With the advancement of emerging power-to-gas (P2G) technologies, the integrated power and gas distribution system (IPGDS) with bidirectional energy conversion is becoming a promising measure to promote the integration of renewable-based distributed generation. This study proposes the linear network model for the IPGDS and takes the reactive power consumption of P2G into account. The linear model considering the network constraints of the IPGDS is implemented combining the Wendroff difference for gas network equations and the pyramidal approximation for power network equations. With the proposed linear network model, the economic dispatch of the IPGDS is studied considering distribution network reconfiguration. Both the overall positive role of P2G and the negative effect of P2G's reactive power consumption are analysed. The former helps to mitigate the voltage violations and to improve economic efficiency, while the latter reduces these benefits. Case studies in the integrated 33-bus power and 12-node gas distribution system validate the effectiveness and applicability of the proposed linear network model of the IPGDS and the necessity of considering P2G's reactive power consumption.

Optimal control of PV–WS battery-based microgrid using an adaptive water cycle technique

This paper presents a new adaptive water cycle algorithm (AWCA) to optimize the controllers operation in a multiple distributed generators (DGs)-based microgrid. The inconsistent nature of renewable-based DG creates several challenges to stability margin of microgrid. The proposed AWCA improves the stability margin and transient profile of multi-DGs-based microgrid during various bounded/unbounded uncertainties. AWCA includes a sinusoidal chaotic map to handle all the nonlinearities produced in the microgrid. A multiple DG-based IEEE 9-bus microgrid system is considered to validate the performance of the proposed AWCA. Wind-generating system (WS) and photovoltaic (PV) with auxiliary battery energy storage are integrated to the microgrid as different DGs. A diesel engine generator with Pade approximation-based governor control is incorporated to provide uninterrupted microgrid operation. Here, second-order phase-locked loop-based feedback controllers are used for the grid synchronization of the DGs. The controller parameters of rotor-side converter and grid-side converter of WS are optimized to provide a better power regulation at the PCC. Similarly, the PV-VSC controller parameters are optimized by AWCA to improve the overall operation of the microgrid by minimizing the loss and improving stability. The performance of the proposed AWCA is verified under different DG-side as well as microgrid uncertainties. The efficacy of the proposed AWCA is presented in terms of power oscillation damping and improved stability limits. The assessments are evaluated in MATLAB script and dSPACE 1104-based hardware-in-loop platforms.

Deep Learning Detection of Electricity Theft Cyber-Attacks in Renewable Distributed Generation

Unlike the existing research that focuses on detecting electricity theft cyber-attacks in the consumption domain, this paper investigates electricity thefts at the distributed generation (DG) domain. In this attack, malicious customers hack into the smart meters monitoring their renewable-based DG units and manipulate their readings to claim higher supplied energy to the grid and hence falsely overcharge the utility company. Deep machine learning is investigated to detect such a malicious behavior. We aim to answer three main questions in this paper: a) What are the cyber-attack functions that can be applied by malicious customers to the generation data in order to falsely overcharge the utility company? b) What sources of data can be used in order to detect these cyber-attacks by the utility company? c) Which deep machine learning-model should be used in order to detect these cyber-attacks? Our investigation revealed that integrating various data from the DG smart meters, meteorological reports, and SCADA metering points in the training of a deep convolutional-recurrent neural network offers the highest detection rate (99.3%) and lowest false alarm (0.22%).

An economic-environmental asset planning in electric distribution networks considering carbon emission trading and demand response

Initiatives such as government programs and investment in low-carbon technologies have been adopted to mitigate the carbon emissions in the electricity sector. These initiatives have resulted in new challenges in the power sector, and to address them adequately, innovative frameworks are required in the electric distribution network (EDN) expansion planning and operation problems. Therefore, this work proposes an environmentally committed asset planning approach to remedy the existing issues to some extent. The proposed strategy investigates the benefits of the simultaneous allocation of several assets such as capacitor banks (CBs), distributed generation (DG) units based on renewable energy sources, and energy storage systems (ESSs). Moreover, an innovative carbon emission trading scheme is formulated in the planning stage to mitigate the CO2 emissions while a demand response program is applied to modify the consumption behavior. The proposed approach is formulated as a two-stage robust mixed-integer programming model, which considers uncertainties associated with the electricity demand and renewable-based DG. To cope with the difficulties of this complex model, utilizing an efficient decomposition algorithm, such as the C&CG decomposition algorithm, is essential. The potential of the proposed approach is studied under different operating conditions and via several test cases on a 137-node EDN. In addition, to validate the performance of the proposed carbon emission scheme, a multi-region 54-node distribution network is adequately evaluated. Results show that by considering simultaneously multiple planning alternatives, carbon emission trading scheme, and the demand response program, the total CO2 emissions are reduced by up to 15%.

Power Quality Assessment of Distorted Distribution Networks Incorporating Renewable Distributed Generation Systems Based on the Analytic Hierarchy Process

The proliferation of not only power electronics supported consumption technologies but also the expansion of the renewable-based distributed generation (DG) systems has given rise to severe power quality (PQ) phenomena in consort with the offered technical, economic and environmental benefits under deregulated environment. The forthcoming complexity of distribution power networks caused by incorporation of a large number of DG units in deregulated electricity market unquestionably makes PQ assessment procedure a quite cumbersome one. In present work, an analytic hierarchy process (AHP) inspired methodology is proposed for PQ assessment of distorted distribution power systems under the presence of renewable-based DGs. The proposed PQ assessment approach is based on formulating a unified power quality index (UPQI) for assessing the overall PQ performance of individual buses of the network along with the entire distribution network (DN) considered taking four PQ phenomena, viz. voltage harmonics, voltage sags, voltage unbalance and steady-state voltage profile at each bus into account. t. The application significance of the presented methodology is established by utilizing it on an IEEE 13 bus test distribution system modified through incorporating the nonlinear loads and DG systems based on three types of RES namely, photovoltaic (PV), wind and fuel cell, in MATLAB/Simulink environment. The results achieved validates the efficacy of the presented approach in assessing the overall PQ performance of each of the buses and the entire DN along with benchmarking it with respect to the threshold level of unity. Based on obtained results, also the comparative analysis is performed among PQ performances of DN with selected three RES based DGs. Moreover, the impact of the employing the custom power devices (CPDs) as well as excessive penetration level of renewable energy over PQ performance of distribution network, are also investigated by the application of the formulated index.

Probabilistic power flow analysis of microgrid with renewable energy

With the development of renewable-based distributed generation (RDG), there are increasing uncertainties in the operation of microgrids (MGs), and stochastic evaluation methods are attracting more attention nowadays. In this paper, a probabilistic power flow (PPF) analysis method is proposed to evaluate the influence of uncertainties on the power flow of MGs. First, the MG PPF model is established considering different operation modes of MGs and uncertainties of RDG and load demands. Then, the Borgonovo method, which is a density-based global sensitivity analysis (GSA) method, is used to evaluate the importance of input variables in PPF calculation. To improve the computational efficiency of GSA, the sparse polynomial chaos expansion (SPCE) is used to establish the surrogate model of MG PPF, and the Borgonovo index is calculated based on the surrogate model. Finally, the procedure of applying GSA to MG power flow is established. The proposed method is tested using 33-node and 123-node MGs, and is compared with other methods to validate its effectiveness. Simulation results indicate that the proposed method identifies critical uncertainties that affect MG power flow. Based on the rankings of input variables, the influences of critical uncertainties are diminished with energy storage systems.

Optimal location-allocation of storage devices and renewable-based DG in distribution systems

This paper proposes a mixed integer conic programming (MICP) model to find the optimal type, size, and place of distributed generators (DG) over a multistage planning horizon in radial distribution systems. The proposed planning framework focuses on the optimal siting and sizing of wind turbines, photovoltaic panels, gas turbines, and energy storage devices (ESD). Inherently, renewable energy sources and electricity demands are subject to uncertainty. To handle such probabilistic situations in decision-making, the MICP model is extended into a two-stage stochastic programming model. To obtain more practical results, annual historical data are used to generate the scenarios. For the sake of tractability, the k-means clustering technique is used to reduce the number of scenarios while keeping the correlation between the uncertain data. Due to convexity, the proposed MICP model guarantees to find the global optimal solution. To show the potential and performance of the proposed model a 69-bus radial distribution system under different conditions is dully studied and a sensitivity analysis is conducted. Results and comparisons approve its effectiveness and usefulness.

Power system expansion planning under global and local emission mitigation policies

This work analyzes the impacts on the power system expansion planning of implementing CO2 and local pollutant emission taxes under five different policy-relevant scenarios. To do this, we have formulated and implemented an optimization model based on a mixed-integer linear program, which determines the optimal expansion plan considering the installation of both large-scale power plants and renewable-based distributed generation. An important characteristic of the proposed model is that it includes a detailed formulation of the power system. Moreover, differently than existing literature, special attention is given to the analysis of the spatial-temporal distributive effects of pollutant taxes, considering both global and local pollutant emissions. The method is applied to the main Chilean power system. Our results indicate that global and local pollutant taxes significantly impact both planning and operational decisions in the power system. In particular, pollutant taxes may have significant spatial distributive effects, as shown in the analysis of 13 regions of Chile, leading to damages in some specific regions while relatively benefiting others. Our results also show that the availability of renewable energy capacity may improve the effectiveness of pollutant taxes. Particularly, adding 1.5 GW of hydro capacity to the Chilean system allows avoiding around 32 GWh of fossil fuel generation per year, saving more than 1.5 billion US$ in the 10-year horizon considered. The proposed method and qualitative results are sufficiently generic to apply to any other jurisdiction.

Protective Device and Switch Allocation for Reliability Optimization With Distributed Generators

The location of protective devices, such as circuit breakers, reclosers, sectionalizers, and fuses, along with isolating switches in a distribution network is a key factor impacting the reliability performance. Furthermore, automatic restoration from intentional islanding with renewable-based distributed generators (DGs) or from alternate feeders can reduce outage times. In this paper, a mixed-integer linear program (MILP) formulation is proposed for protective device and switch allocation considering intentional islanding with distributed generation in distribution systems. The specific impact of each protective device type and isolating switch is modeled, e.g., momentary interruptions caused by reclosers. Efficient graph search algorithms combined with a directed graph representation of the distribution system allows for preprocessing of the network data and facilitates the formulation of an MILP. The formulation is able to efficiently compute optimal device allocations for multiple scenarios, revealing key insights, e.g., the location and capacity of DGs providing the greatest reliability benefit for a fixed protection budget. Numerical tests on realistic feeders and comparison with prior solutions show improved device allocations and lower objective function values.

Power loss minimisation of rural feeder of Jaipur city by renewable-based DG technologies

ABSTRACTPower systems are becoming more complex day by day due to stressed situation in a distribution system, augmented growth in population and high demands of electrical power. In India, the distribution losses are around 20–25%. Distributed Generation (DG) units are broadly used to reduce the power losses in distribution system. To optimise the benefits of DGs, it is essential to determine its appropriate size and location. A new mathematical expression, power voltage sensitivity constant, has been formulated to determine its size and location. In this paper, two types of DG units (PV module and shunt capacitor) are employed for power loss reduction. The size of DG units (PV module) has been restricted to 50% of total system load in the paper. The proposed approach is tested on standard IEEE 69 bus distribution system and 130 bus real distribution system of Jamwa Ramgarh village, Jaipur, India. The results obtained from standard systems are compared with latest optimisation techniques to show the effectiveness and robustness of the proposed approach. The results of real systems are also encouraging.

Optimal siting and sizing of renewable energy sources, storage devices, and reactive support devices to obtain a sustainable electrical distribution systems

This paper presents an integrated planning framework to optimally determine the location and allocation of renewable-based distributed generation (DG) units, energy storage systems (ESSs), and capacitor banks (CBs). This planning aim at improving the performance of electrical distribution systems (EDSs). In the proposed model, the cost of energy delivered by the substation and the investment costs are minimized. The environmental aspects are taken into account to obtain an efficient environmentally committed plan. The uncertainties due to PV generation and demand profile are considered via external uncertainty indexes in a deterministic environment. The proposed model is a mixed-integer nonlinear programming (MINLP) problem, which is recast to a mixed-integer linear programming (MILP) problem using appropriate linearization techniques. This approximated MILP model is implemented in the mathematical language AMPL, while the commercial solver CPLEX is used to obtain the global optimal solutions. The proposed model is validated by testing on a medium voltage distribution system with 135 nodes under different conditions and topologies.

A Consensus-Based Distributed Computational Intelligence Technique for Real-Time Optimal Control in Smart Distribution Grids

In real-time large-scale optimization problems, such as in smart grids, centralized algorithms may face difficulties in handling fast-varying system conditions, such as high variability of renewable-based distributed generators (DGs) and controllable loads (CLs). Further, centralized algorithms may encounter computation and communication bottlenecks while handling a large number of variables. To tackle these issues, consensus-based distributed strategies have been proposed recently. However, distributed computational intelligence (CI)-based techniques can provide a much better near-optimal solution within fewer iterations of the algorithm, which is a critical requirement in smart grids. Therefore, in this paper, a consensus-based dimension-distributed CI technique is proposed for real-time optimal control in smart distribution grids in which a large number of DGs and CLs are present. The proposed approach considers each DG or CL as a separate private entity, which is more relevant from the perspective of smart grid optimization. In the proposed consensus-based framework, each DG or CL is associated with an agent, and each agent is allowed to communicate only with its neighboring agents. The effectiveness of the proposed approach in terms of convergence, adaptability, and optimality with respect to a centralized algorithm and a benchmark algorithm is shown through simulations on 30-node and 119-node distribution test systems.

A New Risk-Managed Planning of Electric Distribution Network Incorporating Customer Engagement and Temporary Solutions

The connection of renewable-based distributed generation (DG) in distribution networks has been increasing over the last few decades, which would result in increased network capacity to handle their uncertainties along with uncertainties associated with demand forecast. Temporary non-network solutions (NNSs) such as demand response (DR) and temporary energy storage system/DG are considered as promising options for handling these uncertainties at a lower cost than network alternatives. In order to manage and treat the risk associated with these uncertainties using NNSs, this paper presents a new risk-managed approach for multi-stage distribution expansion planning (MSDEP) at a lower cost. In this approach, the uncertainty of available DR is also taken into account. The philosophy of the proposed approach is to find the “optimal level of demand” for each year at which the network should be upgraded using network solutions while procuring temporary NNSs to supply the excess demand above this level. A recently developed forward–backward approach is fitted to solve the risk-managed MSDEP model presented here for real sized networks with a manageable computational cost. Simulation results of two case studies, IEEE 13-bus and a realistic 747-bus distribution network, illustrate the effectiveness of the proposed approach.

Coordinated dispatch of networked energy storage systems for loading management in active distribution networks

System overloading is becoming a critical issue in distribution system due to outdated infrastructure and growing electricity demand. Although renewable-based distributed generation is a promising solution to relieve this issue, its intermittency and uncertainty impose significant challenges on system operations. This study attempts to coordinate networked energy storage systems (NESSs) to manage network loading in distribution networks. The NESS can act as a buffer to absorb surplus energy during high generation periods and serve the demand during peak load periods. A consensus-based dispatch strategy is proposed to coordinate NESSs. Through limited communication among neighbouring NESSs, required active power curtailment is shared. The mathematical formulation of a state-of-charge weighting factor is introduced to improve the efficiency of NESSs. A sensitivity study is also conducted to demonstrate the performance of the proposed strategy.