IEEE 33-bus Distribution Test System Research Articles

A multi-stage linearized interactive operation model of smart distribution grid with residential microgrids

This paper addresses an interactive energy scheduling model developed between distribution system and residential microgrids (RMGs). To make such concepts, a three-stage mathematical programming framework is proposed. Residential energy management (REM) approach which is in interaction with residential microgrid operator (RMGO) is developed which contains two stages, sequentially. The main goal of these stages is to modify RMGs day-ahead load profile by considering the minimized total daily energy expenses of each home at each RMG. Moreover, in these stages, in addition to existing fixed-loads at each evaluated residential homes, in-home energy management (iHEM) systems are responsible for adjusting the shiftable appliances and small scale distributed energy resources (DERs) commitment. In third stage, besides the interactions between distribution system operator (DSO) and RMG operators (RMGOs), optimal operation cost of the distribution system is determined as well. In this way, optimal scheduling of distribution system active elements namely large scale DERs are considered and the changing trends in energy exchanges, power losses, and voltage profile are addressed. To lower the computational burden of the proposed model, linearization techniques are applied in the proposed model. Simulation studies are reported on modified IEEE 33-bus distribution test system to assess the performance of the proposed model. Results are discussed in depth.

A Multi-Market-Driven Approach to Energy Scheduling of Smart Microgrids in Distribution Networks

In order to coordinate the economic desire of microgrid (MG) owners and the stability operation requirement of the distribution system operator (DSO), a multi-market participation framework is proposed to stimulate the energy transaction potential of MGs through distributed and centralized ways. Firstly, an MG equipped with storage can contribute to the stability improvement at special nodes of the distribution grid where the uncertain factors (such as intermittent renewable sources and electric vehicles) exist. The DSO is thus interested in encouraging specified MGs to provide voltage stability services by creating a distribution grid service market (DGSM), where the dynamic production-price auction is used to capture the competition of the distributed MGs. Moreover, an aggregator, serving as a broker and controller for MGs, is considered to participate in the day-ahead wholesale market. A Stackelberg game is modeled accordingly to solve the price and quantity package allocation between aggregator and MGs. Finally, the modified IEEE-33 bus distribution test system is used to demonstrate the applicability and effectiveness of the proposed multi-market mechanism. The results under this framework improve both MGs and utility.

A hybrid teaching–learning-based optimization technique for optimal DG sizing and placement in radial distribution systems

Distributed generation (DG) technology has proved to be an efficient and economical way of generation of power. DGs are intended to generate power near the load centers. Optimal allocation of DG resources enhances the overall performance of distribution systems. This paper presents a hybrid teaching–learning-based optimization (HTLBO) technique for the optimal allocation of DGs in distribution systems. The proposed technique is proficient in handling continuous as well as discrete variables and has the capability to escape strong local minima/maxima trappings. The validity and effectiveness of HTLBO are tested on well-defined standard mathematical benchmark functions. The proposed method is further implemented for optimal allocation of DGs in the IEEE 33-bus, 69-bus and 118-bus radial distribution test systems for minimization of power losses, voltage deviation and maximization of voltage stability index. The multi-objective function for DG allocation uses the ɛ-constraints approach. The obtained results reveal improved convergence characteristics over both teaching–learning-based optimization and quasi-oppositional teaching–learning-based optimization.

Optimal Charging Schemes for Electric Vehicles in Smart Grid: A Contract Theoretic Approach

Due to their environment friendliness, electric vehicles (EVs) are anticipated to form a considerable fraction of vehicles for transportation in smart cities. It is essential to design an electricity charging scheme that takes the utilities of both the charging stations and the EVs into consideration. However, the self-interested nature of the EVs together with the information asymmetry between the energy demand and supply sides makes the design a significant challenge. In this paper, we propose a queuing network-based model to characterize the charging process of the multiple EVs in a renewable energy-aided charging station. Based on the model, we adopt a contract theoretic approach to design an optimal charging policy in an information asymmetry scenario. Furthermore, we propose the new contract-based charging rate assignment and admission control schemes that maximize the utility of the charging station under certain charging constraints. To derive the optimal contract, we present a two-step iterative algorithm and prove its convergence. We evaluate the proposed schemes based on the IEEE 69-bus distribution test system. Results indicate that the contract-based charging schemes can effectively benefit both the charging stations and the EVs and concurrently improve the load level of the smart grid.

Optimal simultaneous day-ahead scheduling and hourly reconfiguration of distribution systems considering responsive loads

This paper develops an optimal simultaneous hourly reconfiguration and day-ahead scheduling framework in smart distribution systems considering the operation of the protection devices. The objective function of the model is defined as the minimization of system’s costs in terms of the costs associated with the purchased power from the wholesale market as well as the distributed generation (DG) owners, cost of switching actions, power losses cost and the cost for implementation of demand response (DR) programs. Moreover, a novel switching index along with the maximum number of switching actions based on the switch ages and critical branches in the network is presented. Due to the nonlinearity and non-convexity nature of the problem, the proposed optimization problem is then solved using a metaheuristic approach based on particle swarm optimization (PSO). As the result of the optimization process, the optimal set-points of DGs and responsive loads together with the optimal radial configuration of the distribution system for each hour of the scheduling time horizon are determined. To investigate the effect of DR programs and hourly reconfiguration on the load profile of the system, different price-based DR actions combined with interruptible load programs are also considered. Moreover, to demonstrate the satisfactory performance of the proposed model, the IEEE 33-bus distribution test system is thoroughly interrogated.

A comprehensive technique for optimal allocation of distributed energy resources in radial distribution systems

Distributed generation (DG) is a better alternative to meet power demand near the load centers than centralized power generation. Optimal placement and sizing of DGs plays a crucial role in improving the performance of distribution systems in terms of network loss reduction, voltage profile improvement, reliability of power supply and stability issues. This paper presents a comprehensive teaching learning-based optimization (CTLBO) technique for the optimal allocation of DGs in radial distribution systems to improve network loss reduction, voltage profile and annual energy savings. The proposed technique can handle mixed integer variables, is parameter independent and possesses immunity to local extrema trappings. The effectiveness of the proposed method is first validated on standard mathematical benchmark functions. It is observed to have better convergence characteristics than teaching learning-based optimization (TLBO) and quasi-oppositional teaching learning-based optimization (QOTLBO). Subsequently, it is applied to optimal DG allocation in IEEE 33-bus, 69-bus and 118-bus radial distribution test systems. Both single and multi-objective formulations are considered. In addition, the selection of the optimal number of DGs in the distribution networks is also investigated and case studies are carried out. Results demonstrate that optimal allocation of DGs using the proposed technique results in marked improvement in the performance of distribution systems over TLBO and QOTLBO. The applicability of the proposed technique for DG allocation in distribution systems with practical load profiles results in further improvement in annual energy loss reduction and cost savings.

Profit-Based DG Planning Considering Environmental and Operational Issues: A Multiobjective Approach

In this paper, a methodology is presented to find the optimal size, location and technology of distributed generation (DG) units considering economic, technical, and environmental issues simultaneously. This technique can be implemented to both renewable and nonrenewable DGs. From economic perspective, minimizing cost and maximizing profit are investigated separately and compared with each other. In addition, from technical and environmental viewpoints, a complete set of indices are defined. In order to find the optimal solution, multiobjective particle swarm optimization, and fuzzy decision-making techniques are utilized. Furthermore, to evaluate the proposed method more accurately, two scenarios are defined. In fact, the first scenario, which is only based on cost minimization and technical objective functions, plays an introductory role for better comprehension of the second scenario. In the second scenario, which includes the presentation of the methodologies of this paper, the effect of selecting either profit maximization or cost minimization as an economic objective function along with the minimization of technical and environmental objective functions are precisely investigated. By carrying out the simulations on the IEEE 33-bus distribution test system, it is concluded that the cost-based framework is not the most profitable one, whereas the profit-based one may well lead to a more appropriate result from economic viewpoints.

Probabilistic voltage stability assessment of distribution networks with wind generation using combined cumulants and maximum entropy method

From the voltage stability viewpoint, a proper probabilistic analysis of the active distribution networks is essential for distribution system operator to identify and rank the weak buses of system. This paper introduces a probabilistic voltage stability index (VSI) to study the local stability of radial distribution networks including wind generation uncertainty. This index can identify the most sensitive bus to the voltage collapse. The proposed model combines the cumulants with the maximum entropy technique based on a backward/forward sweep technique. The suggested method evaluates not only the voltage magnitude, but also the voltage stability condition of each node in a probabilistic manner. Furthermore, the uncertainties of distributed generation and load demand are taken into account by this index. The proposed methodology is applied to IEEE 33-bus and 69-bus distribution test systems, and the results are interpreted. Four scenarios are studied to assess the impacts of substation voltage changes and different probability density functions of load on the distribution functions of voltage and VSI. Moreover, the effects of voltage dependent load model on the probabilistic VSI are studied. The results of different case studies have demonstrated that the proposed approach is fast and accurate.

Impact of Distributed Generation on the Fault Current in Power Distribution System

<p>Monitoring fault current is very important in power system protection. Therefore, the impact of installing Distributed Generation (DG) on the fault current is investigated in this paper. Three types of fault currents which are single line-to-ground, double line-to-ground and three phase fault are analyzed at various fault locations. The optimal location of DG was identified heuristically using power system simulation program for planning, design and analysis of distribution system (PSS/Adept). The simulation was conducted by observing the power losses of the test system by installing DG at each load buses. Bus with minimum power loss was chosen as the optimal location of DG. In order to study the impact of DG to the fault current, various locations and sizes of DG were also selected. The simulations were conducted on IEEE 33-bus distribution test system and IEEE 69-bus distribution test system. The results showed that the impact of DG to the fault current is significant especially when fault occurs at busses near to DG location.</p>

Probabilistic load flow computation using saddle-point approximation

PurposeThe purpose of this paper is to present a new cumulant-based method, based on the properties of saddle-point approximation (SPA), to solve the probabilistic load flow (PLF) problem for distribution networks with wind generation.Design/methodology/approachThis technique combines cumulant properties with the SPA to improve the analytical approach of PLF calculation. The proposed approach takes into account the load demand and wind generation uncertainties in distribution networks, where a suitable probabilistic model of wind turbine (WT) is used.FindingsThe proposed procedure is applied to IEEE 33-bus distribution test system, and the results are discussed. The output variables, with and without WT connection, are presented for normal and gamma random variables (RVs). The case studies demonstrate that the proposed method gives accurate results with relatively low computational burden even for non-Gaussian probability density functions.Originality/valueThe main contribution of this paper is the use of SPA for the reconstruction of probability density function or cumulative distribution function in the PLF problem. To confirm the validity of the method, results are compared with Monte Carlo simulation and Gram–Charlier expansion results. From the viewpoint of accuracy and computational cost, SPA almost surpasses other approximations for obtaining the cumulative distribution function of the output RVs.

Inbreeded GA for optimal distributed resource allocation in distribution systems

Although the phenomenon of inbreeding is quite usual in animals, but it has not yet been attempted to enhance the performance of genetic algorithms (GAs). The crossover operator of GA can be modified using the concept of inbreeding in animals. This single measure enhances the pace of GA. However, GA stagnates due to inbreeding, as in animals, which is then overcome by suggesting mandatory mutation in addition to the conventional mutation. This study proposes inbreeded GA (IGA) which is then applied to efficiently solve the simultaneous optimal allocation of shunt capacitors and distributed generations in distribution systems while considering realities of practical distribution systems. The objective considered is to maximise annual energy loss reduction. The proposed method is applied on the benchmark IEEE 33-bus test distribution system. The application results obtained shows the supremacy of IGA over GA and other existing metaheuristics.

Multi-Objective Scheduling of Electric Vehicles in Smart Distribution Network

Due to the energy savings and environmental protection they provide, plug-in electric vehicles (PEVs) are increasing in number quickly. Rapid development of PEVs brings new opportunities and challenges to the electricity distribution network’s dispatching. A high number of uncoordinated charging PEVs has significant negative impacts on the secure and economic operation of a distribution network. In this paper, a bi-level programming approach that coordinates PEVs’ charging with the network load and electricity price of the open market is presented. The major objective of the upper level model is to minimize the total network costs and the deviation of electric vehicle aggregators’ charging power and the equivalent power. The subsequent objective of the lower level model after the upper level decision is to minimize the dispatching deviation of the sum of PEVs’ charging power and their optimization charging power under the upper level model. An improved particle swarm optimization algorithm is used to solve the bi-level programming. Numerical studies using a modified IEEE 69-bus distribution test system including six electric vehicle aggregators verify the efficiency of the proposed model.

Decentralized reactive power control of distributed PV and wind power generation units using an optimized fuzzy-based method

The presence of power electronic-based wind turbines and photovoltaic systems in distribution networks has provided distribution companies an opportunity to implement voltage control through using the reactive power of these systems. In this paper, a decentralized method based on fuzzy logic is proposed to control the reactive power of distributed generations (DGs) regarding the technical constraints. The fuzzy system is optimized by gradient descent algorithm (GDA) and then implemented on various DG technologies including a photovoltaic (PV) system, permanent magnet synchronous generator (PMSG) wind turbine and also a doubly fed induction generator (DFIG) wind turbine. The system under study is tied to a real distribution network. Having simulated the system, the paper shows that the fuzzy system can appropriately determine the desired reactive power that should be produced by each DG based on the voltage variation of the bus at which the DG is connected. Furthermore, a centralized voltage control is also applied to the same network to verify the performance of the method proposed. The verification indicates that the method is capable of finding the near-optimal solution. A scenario in which an unwanted conflict appears in the DGs’ function is defined in detail and then a strategy is presented to resolve the situation. In addition to this, the coordination between the stator of the wind turbines and grid side converter (GSC) is examined. To investigate the robustness of the proposed method in different distribution networks, simulation results are also presented for IEEE 33-bus distribution test system. The numerical results show that the fuzzy system can effectively control the voltage of the DG connection bus.

Optimal Allocation of Distributed Energy Resources Using Improved Meta-heuristic Techniques

The optimal allocation of distributed energy resources is one of the most important and challenging task toward realizing smart grid objectives. Smart grid initiatives may be realized after obtaining integrated solutions of distributed energy resources while taking into account the realistic operational strategy of distribution network reconfiguration. This article addresses improved variants of three meta-heuristic techniques—the improved genetic algorithm, improved particle swarm optimization, and improved teaching–learning-based optimization—to efficiently handle the problem of simultaneous allocation of distributed energy resources, such as shunt capacitors and distributed generators in radial distribution networks. The problem is formulated to maximize annual energy loss reduction and to maintain a better node voltage profile while considering network reconfiguration under a variable load scenario. Several algorithm specific modifications are suggested in the standard forms of genetic algorithm, particle swarm optimization, and teaching–learning-based optimization to overcome their intrinsic flaws. In addition, an intelligent search algorithm is proposed to further enhance the performance of optimizing techniques. The proposed methods are investigated on the benchmark IEEE 33-bus test distribution system, and a comparative analysis is carried out to judge the suitability of the proposed techniques. The application results obtained are promising when compared with other established methods.

An integrated approach for distributed resource allocation and network reconfiguration considering load diversity among customers

Abstract In this paper a new dedicated search teaching learning based optimization (DSTLBO) method is proposed for simultaneous allocation of distributed resources keeping in view the practical aspect of network reconfiguration to maximize annual energy loss reduction while maintaining a better node voltage profiles. The accuracy of distributed resource allocation and their benefits depend on proper modeling of system loads. Distribution system planners usually provide dedicated feeders to different classes of customers; each has its own characteristic load pattern which varies hourly and seasonally. There exists diversity in customers’ load demands. Therefore proper modeling of annual load profile is crucial to obtain realistic allocation of distribution resources (shunt capacitors and distributed generations). In the proposed formulation of the optimization problems the diversity in customers’ load patterns has been taken into account. In the proposed dedicated search teaching learning based optimization (DSTLBO), new learning approaches are suggested to enhance convergence, accuracy and efficiency of the standard TLBO. The proposed method is investigated on the benchmark IEEE 33-bus test distribution system. The application results are presented.

Distribution Network Power Loss Minimization by Optimal DG Allocation

This paper presents a methodology based on analytical approach to optimally allocate (site and size) the Distributed Generation (DG) units(s) in radial power distribution networks for minimizing the real power losses. The proposed method requires only the results of base case load flow to determine the optimal size of DG unit(s) required at each bus. For this, suitable analytical expressions have been proposed to determine the optimal size of DG unit(s) to cause total minimum real power loss in a given distribution network with their corresponding optimal location(s). Two cases with two scenarios comprising of DG type and number of DG units (single and multiple), respectively, are considered. The proposed method is applied to an IEEE 33-bus radial distribution test system. Results obtained by this proposed method validate the suitability and importance of appropriate DG allocation and also the number of DG units in power distribution networks.

Asymptotically Optimal Scenario-based Multi-objective Optimization for Distributed Generation Allocation and Sizing in Distribution Systems

Suitable location and optimal sizing are impact on voltage stability margin of the distributed system. It is important to accurately simulate the random output active power of Distributed Generation (DG). In order to model uncertainties of intermittent distributed generation and load, this paper proposes a multi-scenario tree model of windphotovoltaic-load using multiple scenarios technique based on the Wasserstein distance metrics, which generates asymptotically optimal scenario. And in this paper, a multiobjective optimizes control model with scenario tree is presented, which including objectives that are the total active power losses and the voltage deviations of the bus. Moreover, a new hybrid Honey Bee Mating Optimization and Particle Swarm Optimization (HBMO-PSO) algorithm is proposed to solved the problems. In the HBMOPSO algorithm, the mating process is corrected, which the PSO algorithm is combined with the HBMO algorithm to improve the performance of HBMO. Finally, a typical IEEE 33-bus distribution test system is used to investigate the feasibility and effectiveness of the proposed method. Simulation results illustrate the correctness and adaptability of the proposed model and the improved algorithm.

Electricity retail market model with flexible price settings and elastic price-based demand responses by consumers in distribution network

This paper presents a novel electricity retail market model in which elastic demands of consumers in a distribution network are traded at flexible selling prices offered by a retailer. The main works of this paper are in three points: (1) Flexible and divided selling price settings over one day by the retailer, (2) flexible and elastic responses corresponding to the selling prices (price-based Demand Response: DR) by different types of consumers, and (3) distribution network physical constraints to obtain the realistic cost of tariff for usage of the distribution network imposed to the retailer are formulated. Unlike previous related works, the proposed model is a new one applicable to the behavior analysis of decision makers in the deregulated environment with such flexible transactions. In the transactions, the retailer offers a selling price for a unit time period over one day and the consumers elastically respond to the prices. Assuming that the consumers respond to the prices rationally and control their demands flexibly, we model the transaction as a Stackelberg game formulated by a bi-level programming problem. The behavior of the market players is examined in computational experiments using the spot price data in Japan Electricity Power eXchange (JEPX) and distribution network models, i.e. modified IEEE 13 bus test system model and IEEE 33-bus radial distribution test system model. We employ a genetic algorithm to find an approximated solution of the formulated non-convex bi-level programming problem. The computational results show some new findings about the deregulated retail market if flexible transactions between the retailer and the consumers are realized.

Optimal energy management of smart renewable micro-grids in the reconfigurable systems using adaptive harmony search algorithm

This paper suggests a new intelligent stochastic optimisation framework to assess the optimal energy management issue of the renewable micro-grids MGs in the reconfigurable networks. The proposed method is constructed based on the harmony search HS algorithm in addition to a novel adaptive modification method to answer the problem successfully. The suggested modification method includes three sub-modifications to help the algorithm for escaping from the local optima. Regarding the uncertainty effects, Monte Carlo simulation is used to model the uncertainties of the problem. The objective functions to be considered are minimisation of power losses, voltage deviation, total cost and total emission. The high ability and sufficient performance of the proposed stochastic framework are tested on the IEEE 33-bus distribution test system with a typical MG which includes different kinds of renewable energy sources such as photovoltaics, micro-turbine, fuel-cell, wind turbine and battery as the storage device.

Distributed generations planning using flower pollination algorithm for enhancing distribution system voltage stability

Abstract Distributed generations (DGs) have been utilized in some electric power networks. Power loss reductions, voltage improvement, increasing reliability, postponement of system upgrading and environmental friendliness are some advantages of DG-unit applications. This paper presents a new optimization approach that employs a flower pollination algorithm (FPA) to determine the optimal DG-unit’s size and location in order to minimize the total system real power loss and improve the system buses voltage. The FPA is a new metaheuristic optimization technique and it is inspired by the reproduction strategy of the flow pollination process of flowering plants. To reveal the validity of the FPA algorithm, IEEE 33-bus, 69-bus and 136-bus radial distribution test systems are examined with different test cases of the objective function using the MATLAB. Furthermore, the results obtained by the proposed FPA algorithm are compared with other metaheuristic optimization techniques such as backtracking search optimization algorithm, artificial bee colony, and selection algorithm. The outcomes verify that the FPA algorithm is efficient, robust, and capable of handling mixed integer nonlinear optimization problems.