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Summary The goal of this paper is to optimize energy consumption, with minimum consumer interaction, and least impact on his/her life style, comfort and convenience. In the literature different demand side management (DSM) schemes are employed for consumer demand management. Till now, to the best of our knowledge none of researchers considered the consumers preferences, priorities, ease of use, grid stability, deviation minimization, demand curve flattening and implantation cost, collectively in a single management scheme. In this paper we proposed an innovative, cost-effective solution, to overcome most of the shortcomings of the previous solutions. In addition, it would also provide consumers' privacy as it would mask the energy usage pattern that combines psychological incentives in addition to economic benefits. The proposed Smart Energy-consumption Management System Considering Consumers' Spending Goal (SEMS-CCGS) can be used for optimized electricity consumption at any premises (residential, enterprise, commercial, etc.). The preliminary simulations showed 25.6% of electricity cost saving using SEMS-CCGS. The proposed system is appraised valuable by the utility companies whose focus is to shave peak loads. It also plays an important role in grid stability by improving the diversity factor and deviation of consumers load profile from available load curve. The simulation shows that deviation is minimized up to 45%. Copyright © 2015 John Wiley & Sons, Ltd.

Summary This paper proposes a bilinear robust state estimator (BRSE), which includes a linear programming problem and a quadratic programming problem with a nonlinear transformation in between. The main advantages of BRSE are threefold: (i) it can automatically suppress bad measurements in the estimation process effectively, thus possessing good robustness; (ii) it is globally convex, theoretically guaranteeing a global optimization for state estimator; and (iii) because no nonlinear iterative algorithms are required to solve the linearized model, there is no convergence problem. To further improve BRSE's ability to inhibit leverage bad measurements, this paper also proposes a robust weighted least absolute value estimation with optimal transformations, which can be directly used in BRSE. Simulations are conducted on a rudimentary 3-bus system for validating the proposed methodology and algorithm. Furthermore, benchmark systems including IEEE 9, 14, 30, 39, 57, 118, and 300-bus systems are tested to demonstrate the efficiency and reliability of our methodology and algorithm. Copyright © 2015 John Wiley & Sons, Ltd.

Summary Plug-in hybrid electric vehicles (PHEVs) are becoming more prevalent day by day. The batteries of these electric vehicles may be charged from a standard outlet at home. These extra electrical loads have several impacts on distribution networks, e.g. network energy loss. This paper presents a convex quadratic local optimal solution, instead of non-convex global optimal approach, to minimize the energy loss of distribution system with the different penetration levels of PHEVs. In this paper, it is assumed that vehicle owners can charge their vehicles either at workplace or at home. Furthermore, daily needed energy of PHEVs is simulated based on stochastic modeling of PHEV owners' behavior at any time. Moreover, in this paper it is supposed that PHEVs can be used as reactive power resources in vehicle to grid (V2G) operation mode. The proposed methodology is applied to a realistic distribution network. The results show that network energy loss may be remarkably increases, as the penetration of PHEVs increases without smart charging strategy. Managed and smart charging of PHEVs is needed to minimize the extent of network energy loss. As revealed by the results, smart charging of PHEVs with consideration of V2G operation mode in conjunction with charging at workplace may have the most effect on the network energy loss reduction. Copyright © 2015 John Wiley & Sons, Ltd.

Summary This paper presents a new mixed-integer linear programming model for day-ahead self-scheduling of generating companies integrating the underlying ideas of robust optimization (RO) and stochastic programming (SP) to cope with the uncertainties of electricity market prices and availability/unavailability of units. The proposed hybrid approach models the uncertainty of electricity market prices by bounded intervals instead of probability distributions, aiming to derive a more tractable optimization model. Conservatism against uncertain electricity market prices is adjusted by a certain parameter named budget of robustness. Also, a renovated Markov chain approach considering the chance of return (i.e., return rate) for failed units, in addition to forced outage rate of units, in each hour of the scheduling period is introduced in this paper to produce a set of scenarios modeling the availability/unavailability of units. Therefore, the proposed hybrid self-scheduling approach benefits from both the tractability of RO and modeling accuracy of SP. The proposed approach is implemented on IEEE 118-bus test system under different circumstances to illustrate its effectiveness compared to deterministic self-scheduling model as well as the models only employing RO or SP. Copyright © 2015 John Wiley & Sons, Ltd.

Summary This paper evaluates the impact of wind speed correlation uncertainties among multiple wind farms on optimal integration of these wind energy conversion systems to bulk electric system. The problem is solved by a fuzzy version of technique for order preference by similarity to ideal solution as a proficient multi-criteria decision-making (MCDM) method. The uncertainties are addressed through utilizing triangular fuzzy numbers. The proposed MCDM approach considers emission, interruption, and operation and investment costs as three competent decision-making criteria. A sequential Monte Carlo simulation-based chronological optimal power flow (MCS-COPF) incorporating time series characteristics of wind farms is also proposed in this paper to calculate the aforementioned criteria for each integration plan during planning horizon. In this regard, the autoregressive moving average time series model is used to simulate hourly wind speeds during MCS-COPF process. The proposed MCDM scheme is implemented on the IEEE-24 bus reliability test system through different integration alternatives. Copyright © 2015 John Wiley & Sons, Ltd.

Summary Virtual power plant (VPP) is combining different types of generations and interruptible loads to be able to contribute to the market as a power plant with a significant output. In other words, different power generations are combined in a complementary manner to form a defined generation and a demand profile. Because the most important elements of these operators are distributed generation (DG) units and demand response loads (DRLs), the determination of optimal location, capacity, type, as well as the installation time of DGs, DRLs participation size, and place in the distribution system are the most important challenges of operators. In this paper, the formulation of optimal placement of DG resources and DRLs is presented and solved simultaneously in a distribution system to determine the optimal VPP. For this purpose, the concepts of commercial virtual power plant and technical virtual power plant are introduced first, and then the optimal VPP, which can send its energy bids as a short-term and long-term power scheduling to the power market, is determined. Here, commercial virtual power plant and technical virtual power plant will act jointly as commercial–technical virtual power plant to extract the results of the proposed optimization procedure. In this paper, the binary particle swarm optimization algorithm is used to solve the optimization problem in the distribution system. The proposed method is applied to the IEEE 33-bus distribution test network, and the results confirm the effectiveness of the proposed method. Copyright © 2015 John Wiley & Sons, Ltd.

Summary This paper presents a robust evaluation framework for the application of smart grid technologies in generation expansion planning with enforced uncertainties. Considering the availability of smart grid technologies, mainly including load shifting, cost reduction, and environmental benefits, a deterministic optimization model underlying a robust investment plan is designed as the original generation planning problem that minimizes both cost and emission and subjects to the constraints of the basic plan. The proposed robust optimization methodology is used to explore the robust counterparts of the original planning problem with the uncertainties from the forecast load demand and the availability of new technologies. Moreover, the theoretical issue that the different types of uncertainties lead to different tractable robust measures is addressed by the application of the proposed approach in this paper. The optimal generation planning solution obtained from the presented approach can show the impact of smart grid technologies on investment scheduling under the worst-case uncertainties. The evaluation framework can identify a robustness decision for all realizations of the plan uncertainties, in which simulation results will demonstrate the benefits of the proposed models in case studies. Copyright © 2015 John Wiley & Sons, Ltd.

Summary The major concern of power utilities is to maintain, in all situations, the supply of electrical power to all its customers without any failure. However, because of a recent phenomenon such as demand power increase, insufficient power generation and other economical and environmental factors, most power system utilities operate with its equipment very close to their limits. This situation implies that events such as voltage instability and voltage collapse become more likely to occur. The static synchronous compensator, a shunt-connected Flexible AC Transmission Systems device capable of regulating the voltage level, is an important tool in order to prevent these occurrences from happening. In this paper, an approach to the problem of where these devices have to be inserted and what size should they have in order to enhance the voltage stability margin to a specified level is introduced in the form of metaheuristic approach. The approach was developed using the continuation power flow method to calculate the point of maximum loadability of the power system and using the particle swarm optimization concept to find the optimal combination of locations and sizes of a group of static synchronous compensator units. This is a known approach to the problem, but a new design of the objective function, which seeks to attain a pre-specified desired voltage stability margin, instead of maximizing it, is presented as an original contribution of the paper. Copyright © 2015 John Wiley & Sons, Ltd.

Summary This paper investigates the application of a robust method to solve the problem of Daily Hydrothermal Generation Scheduling (DHGS). The Unit Commitment is implemented in restructured power systems by Independent System Operators to obtain an economic hourly generation schedule intended for the daily-ahead market. The application of Mixed-Integer Non-Linear Programming to deal with the DHGS problem has been discussed in this paper using Information Gap Decision Theory (IGDT) to assess a robust strategy for Independent System Operator. It is noted that the problem's uncertainty is due to load demand characterized using an IGDT method. The IGDT's concept is to model the uncertainty as an unbounded gap in the available information. Besides, this paper presents a model for risk-constrained DHGS to minimize the cost under uncertain load. This approach optimizes the robustness of decision-making strategy. Copyright © 2015 John Wiley & Sons, Ltd.

SUMMARY This paper presents a multi-objective mathematical programming (MMP) model to day-ahead market clearing of joint energy and reserves auctions by integrating a thyristor-controlled series compensator (TCSC) device with the optimization problem developed under normal and contingency cases. The proposed market clearing framework includes minimization of energy and reserves offer cost, congestion rent, TCSC cost, expected interruption cost, line overload, voltage deviation, and loadability limit. A new index called congestion efficiencyindexisproposedforthebestplacementofTCSCundernetworkcontingencyconditions.Traditional MMP methods such as direction scalarization and e-constraint methods scalarize the objective vector into a single objective. Those cases are time-consuming and require a number of runs equal to the number of desired efficient solutions. In this paper, the non-dominated sorting genetic algorithm II (NSGA-II), which is integrated with an adaptive neuro-fuzzy inference system (ANFIS), is proposed to find the solution of the optimal schedule of the units energy and reserves by which the parameters of NSGA-II (probabilities of crossover and mutation) are dynamically set, according to a training process. The proposed methodology is developed on the IEEE 30-bus test system, and the results are compared with fuzzy-based and ordinary NSGA-II methods. These comparisons confirm the efficiency of the developed method. Copyright © 2014 John Wiley & Sons, Ltd.

SUMMARY This paper presents a new solution approach to determine the optimal number and locations of both manual and automatic switches, with respect to probabilities of all feasible control sequences under contingencies. Furthermore, both transformer and bus failure rates are incorporated in the proposed problem formulation to reach more practical results. An artificial bee colony based algorithm is also introduced to solve the optimization problem. Besides, prevalent cost function formulation of switch placement problem is extended by incorporating operation probabilities of switches and the affiliated infrastructures. Bus number four of the Roy Billinton test system is employed to illustrate the effects of the proposed approach on distribution networks reliability. Moreover, performance of the developed algorithm is studied in several scenarios, and the obtained results are compared with those of previous methods. Detailed numerical results and comparisons presented in the paper show that the proposed solution approach could noticeably improve the obtained results with low computational burden; thus, it can be used as an effective tool for joint automatic and manual switch placement within practical distribution networks. Copyright © 2014 John Wiley & Sons, Ltd.

Summary The contemporary concept of Smart Distribution Grid motivates researchers to develop more and more efficient Distribution Management System (DMS) as the basic platform of this concept. This paper deals with power flow as the basic DMS calculation. Procedures for this calculation are usually classified as branch oriented [back/forward sweeping (BFS)] and bus oriented [current injection and Newton/Raphson derivatives]. The first class is favored in this paper. An industry grade generalized BFS (GBFS) procedure is developed for power flow calculation of unbalanced distribution networks. The most complex distribution networks—“irregular” mixed networks—are particularly stressed. In addition, a very simple dealing with inductive and capacitive couplings between parallel overhead lines is presented. It is proven that GBFS is superior in comparison with other power flow procedures tailored for distribution networks—its robustness is at least equal to the robustness of other procedures, but its efficiency is significantly higher. Copyright © 2014 John Wiley & Sons, Ltd.

Summary This paper deals with a grid-connected Home Energy System (HES), including Combined Heat and Power (CHP) and a battery as an Energy Storage System (ESS). The energy routing and scheduling of different resources in this integrated system are optimized to decrease the system operation costs. The optimization procedure leads to generating an efficient look-up table, by which the generated power of each energy resource is determined for all time intervals. Considering the consumption uncertainties, the stochastic load behavior is modeled, and its effect on the system operation cost is investigated. The effects of the electricity tariff and battery on the optimization of the energy resources scheduling are also studied in this paper. Copyright © 2014 John Wiley & Sons, Ltd.

SUMMARY In real case of distribution systems with several directional overcurrent relays, the designer is facing an optimization problem with an extensive search space of discrete values. Finding the optimal solution to such a problem is a time-consuming task with big chance of nonconvexity. In this paper, the optimal coordination of directional overcurrent relays in distribution systems is modeled by attempting the practical issues, that is, full discrete variables for time-current curve, time multiplier setting, and plug setting. To reduce the calculation time, the problem is solved by using a novel method based on network splitting. The problem is solved in two stages: the first one finds the optimal solution for the subnetworks individually and the second one determines the optimal setting for the relays on the tie lines connecting the subnetworks. It is shown that the proposed method improves the relays' operating times and also reduces the chance of nonconvexity of the problem. By applying the proposed method to the 30-bus Institute of Electrical and Electronics Engineers test system, the results are compared with the conventional algorithms. It is found how the practical issues related to the relay characteristics selection can affect the overall relays' time. Also, the computation time is considerably reduced with network splitting method presented in this paper. Copyright © 2014 John Wiley & Sons, Ltd.

Summary When black out occurs, the whole power grid is divided into several subsystems to increase the efficiency and shorten the duration of power system restoration by using parallel restoration strategy. Therefore, reasonable network partitioning in black start phase after large-scale power failures is important in both theory and practice. In this paper, a flexible black start network partitioning strategy considering the subsystem recovery time balance is proposed. Based on the rules of zone division and analyzing initial black start partitioning model of subsystem shortest recovery path, subsystem recovery time balance index is defined to represent disparity of every subsystem's restoration process. Meanwhile, a flexible factor of total recovery time is introduced as part of objective function with the time balance index, aiming at reducing waiting time of the synchronization operation and strengthens efficiency of black start at the cost of the least total time increment. The classic Dijkstra algorithm and improved genetic algorithm are employed to obtain the optimal restoration plan in the paper. Case study on IEEE 118-bus test system shows the validity of the methodology proposed. Copyright © 2014 John Wiley & Sons, Ltd.

Summary This paper suggests a new self-adaptive modification method using firefly algorithm (FA) to investigate the multi-objective probabilistic distribution feeder reconfiguration problem. In this regard, the idea of phase angle vector is employed to replace the traditional Cartesian framework in the FA and thus called θ-FA. Also, a new modification method based on an adaptive mechanism is suggested that will allow each firefly to choose the appropriate modification technique during the optimization suitably. As regards the objective functions, the main focus of this paper is to assess the effect of the reconfiguration on the reliability indices including active power losses, voltage deviation, and system average interruption frequency index. In order to handle the uncertainty effects, a sufficient framework based on 2m + 1 point estimate method is proposed too. The satisfying performance of the proposed method is checked using IEEE 32-bus radial distribution system. Copyright © 2014 John Wiley & Sons, Ltd.

Summary In this paper, the multiple objectives optimization problem in a finite-state model predictive control (FS-MPC) is formulated using fuzzy multi-criteria decision-making. Conventionally, to optimize the multiple objectives in a FS-MPC, the aggregate objective function or a single cost function is constructed, which requires the weighting factor tuning to select the appropriate switching state. Determination of these weighting factors for a particular objective function is a complex and time-consuming task as no systematic procedure is available in literature. The main aim of this paper is to replace the time-consuming task of weighting factor tuning by a simple and systematic procedure, which relies on the relative importance of the individual objective functions. The relative importance of objective functions derived from the expert's knowledge and the desired control objectives is used for appropriate switching state selection. The method is validated with the help of simulation results of a neutral point clamped inverter for the multiple objectives viz. reference current tracking, capacitor voltage balance and switching frequency minimization. The result outcomes of the proposed methodology are compared with the conventional FS-MPC and space vector pulse width modulation based current control schemes. Copyright © 2014 John Wiley & Sons, Ltd.

Summary Conventional transmission congestion evaluations are normally based on deterministic approaches. One of the major disadvantages associated with the deterministic approach is that it is unable to fully reflect the impacts of the random component failures. The deterministic approach can only answer whether a congestion occurs or not, but is unable to quantify the severity of the congestion. This paper presents a systematic congestion evaluation technique using a series of reliability indices including congestion probability, congestion frequency, expected congestion capacity, and energy. These indices can be used to characterize the severity of the congestion at both the transmission component and entire system levels. The proposed approach includes a probabilistic congestion evaluation model and a network solution algorithm taking into account the random failures of power system components, such as generating units, transmission lines, and transformers. The entire evaluation approach is based on the non-sequential Monte Carlo technique. The paper also presents study results on the change of congestion severity of transmission line and overall system using the proposed indices and the effect of some of the parameters on the system congestion. Case studies on the IEEE-RTS indicate that the proposed method is effective and feasible. Copyright © 2014 John Wiley & Sons, Ltd.

SUMMARY This paper presents a novel methodology for constructing wide area thermal security region (WATSR) to monitor the wide area thermal security of bulk power system under “N−1” criterion by utilizing wide area measurement system. A general cutset angle for large-scale inter-area power system is defined and developed as a measurement to assess the system thermal security so as to construct WATSR in cutset-angle space. WATSR consists of all stable operating points under “N−1” criterion, while its boundary is a complete set of all the critical points. In this paper, the boundary of WATSR is represented via its piecewise approximation with a set of hyperplanes to enhance the calculating speed. Hyperplanes are described as a set of linear equalities, which are convenient to be determined. And, because coefficients of a hyperplane can indicate the sensitivities between various control parameters, it is beneficial to power system online security assessment and control strategy optimization. A two-step algorithm is given to apply WATSR to online monitoring for bulk power system. The first step is offline calculation. The aim of this step is to determine a minimum set of hyperplanes to properly describe the boundary of WATSR. A hyperplane is a sub-segment of the security region boundary and normally corresponds to a certain constraint. The second step is online matching. Data retrieved from wide area measurement system are used to determine the relative constraints given by the hyperplanes. And then, the stability of the system can be quickly and accurately determined by the assistance of WATSR result. Once the power system turns to critical, WATSR can help the operators to optimize their control strategy. The methodology of WATSR has been successfully implemented in the China Southern Power Grid, which is known as one of the largest alternating current/direct current parallel transmission system in the world. Feasibility and effectiveness of the presented method have been validated through intensive analyses in China Southern Power Grid. Copyright © 2014 John Wiley & Sons, Ltd.

Voltage and current imbalance have adverse impacts on power systems such as power loss increase, communication interference, and component lifetime reduction. This paper attempts to look at unbalance impacts from distribution system operator's (DSO) viewpoint in order to understand them and then mitigate the impacts. Since solving the unbalance conditions is influenced by the way the imbalance is defined, standard unbalance indexes are studied, and new indexes are proposed in this paper. Re-phasing of the customers is selected to reduce the level of unbalance in distribution feeders. Due to the huge number of customers, a wide variety of choices can be selected for re-phasing of customers, which makes the solution questionable. Therefore, discrete genetic algorithm (DGA) as a metaheuristic method has been utilized in order to distribute customers among the network phases optimally considering the fact that DSO has a limitation for the re-phasing practice. The aim is to reduce the unbalance indexes and power losses throughout the network. Simulations have been carried out on a real test case network, which shows the importance of load balancing and its effects on the power losses, voltage profile, and current flow in that network. The effectiveness of the proposed indexes has also been demonstrated for the four-wire multigrounded distribution system. Since re-phasing of the majority of customers in distribution networks seems impractical, a re-phasing limitation is also investigated in this paper, and some practical suggestions of optimal load balancing in a real-world low-voltage distribution system have been presented here. Results show the importance of load balancing in power loss reduction and voltage unbalance improvement in the low-voltage four-wire multigrounded distribution system. They also illustrate that by changing phases of a few customers, power losses and unbalance indexes will be improved significantly. acceptedVersion

Summary In this paper, a novel with respect to economic dispatch of large-scale multi-area power systems is formulated and studied based on decomposition and coordination algorithm. More precisely, auxiliary problem principle (APP) is applied to overcome the defects of augmented Lagrangian relaxation technology with nonlinear inseparable quadratic terms. The main purpose of this paper is to investigate the intrinsic relationship between APP control parameters and convergence speed, and develop a general criterion that ensures that a suitable control parameter set referring to the implemented APP can be effectively estimated. Theoretical and experimental results of this paper are consistent with each other based on the verification of proposed numerical results. Copyright © 2013 John Wiley & Sons, Ltd.

SUMMARY This paper investigates bidding strategy problems of generation companies in electricity markets. A new bidding model is set up, and a solution approach is presented. Compared with existing models, the model proposed in this paper has some different characteristics. First, the model adopts a bilevel optimization framework where the upper-level optimization is to maximize the profit of generation companies, and the aim of the lower-level optimization is the maximal social welfare and the security of the operation system considered by the Independent System Operator. This model combines practical situations faced by market agents in real-life operations and the requirement of both the system security and the social welfare. Second, the popular risk measure, called conditional value at risk, is used in the bidding model, which can quantify the market risk and describe the competitive mechanism effectively. For solving the complicated bilevel optimization model, a hybrid particle swarm optimization algorithm is presented. Numerical examples are used to validate the model and the algorithm. Copyright © 2013 John Wiley & Sons, Ltd.

SUMMARY The Step Size of the Newton Raphson Method (SSNRM) is based on the optimal multiplier that is used to determine the Multiple Load Flow Solutions (MLFS) for an ill-conditioned power system. However, the SSNRM is incapable of determining the desirable Low Voltage Solution (LVS) from the MLFS at the Maximum Loading Point (MLP), due to the fact that when the load demand increases, the LVSs moves closer to each other at the MLP. Commonly, the smallest optimal multiplier was used to calculate the LVS at the MLP under this condition. However, this paper proves the fact that using the smallest optimal multiplier to determine the most suitable LVS for the systems having multiple solutions at the MLP will not guarantee a favourable outcome. Therefore, this paper investigates the application of the properties of scalar quadratic equations (PSQE) that enhances the function of the SSNRM at the MLP. Thus, an indicator, based on PSQE, is introduced in this work, which amends the existing SSNRM for the purpose of finding the desirable LVS at the MLP. The closest optimal multiplier to the proposed indicator is selected in order to determine the desirable LVS from all possible solutions at the MLP. The proposed method has been tested on a three-bus and IEEE 30-bus systems at the MLP for verification purposes. Additionally, the continuation power flow method is also utilized in order to compare it to the proposed method for the IEEE 30-bus system at the MLP. Copyright © 2013 John Wiley & Sons, Ltd.

SUMMARY How to decide the location and the capacity of distributed generators (DGs) in the distribution network is a complex and hard work. In this paper, several technical indexes involving energy loss, voltage quality and stability, and line loadability are considered to optimize DG capacity and location. It is an optimization problem wherein both discrete and continuous variables are involved, and cloud theory adapted genetic algorithm (CAGA) is employed to avoid search exploration and long time search. The weights of the indexes are decided by judgment matrix. DGs in a 28-node distributed network are employed to be optimized quickly to improve the indexes aforementioned to demonstrate the CAGA developed in this paper. Meanwhile, the optimization results are compared with traditional GA. Copyright © 2013 John Wiley & Sons, Ltd.

SUMMARY This research presents a method for convexifying the non-linear constraints and the objective function for the Transmission Network Expansion Planning Problem (TNEP). The TNEP seeks to identify the best set of transmission capacity additions to meet a future electric power demand. The TNEP is a non-convex Mixed Integer Non-linear Programming problem for which a variety of primal solution methods have been designed. In this paper, the TNEP is formulated as a bilinear programming problem subject to bilinear constraints. This allows resolving the non-linearities through convexification of the non-linear objective function and the constraints for a relaxation of the TNEP in which integrality requirements are removed. The final solution for the original TNEP problem is obtained by using a branch and bound strategy. It was found by using a two-node test case that the convexification method presented in this paper in conjunction with a brand and bound strategy identifies the optimal solution. Successfully solving a small-scale test problem through convexification presents a promising scenario to solve larger scale problems. This primal-dual method allows identifying the duality gap which is a limitation of primal methods. Further multidisciplinary research is needed to extend the method presented here to larger cases and real life networks. Copyright © 2013 John Wiley & Sons, Ltd.

SUMMARY This paper presents a new solution approach for optimal substation expansion planning (SEP) within electric power distribution networks. A modified fuzzy membership matrix as well as a memorable cost index vector is introduced to find the optimal substation service areas. Besides these, a Learning Automata-based algorithm is introduced for simultaneous determination of optimal service areas and capacities of the distribution substations. Electrical constraints such as voltage drops, power flow, radial flow constraints, as well as all prevalent cost indices are taken into consideration. The developed method is conducted to solve the distribution substation allocation problem for an actual distribution network with about 200 000 customers, and obtained results are compared to those of other methods. Detailed numerical results and comparisons presented in the paper show that the proposed solution approach could noticeably improve the quality of problem solutions with low computational burden and can be used as an effective tool for SEP in large distribution networks. Copyright © 2012 John Wiley & Sons, Ltd.

Summary This paper establishes a probabilistic scenario–based framework for the stochastic dynamic economic emission dispatch with unit commitment (SDEED-UC) problem, taking into account wind power uncertainty. To solve the stochastic UC problem, this paper presents a probabilistic scenario analysis approach to find the unit scheduling solution considering all the original scenarios under a predetermined probability level. And a reduced scenario set can be obtained by the simultaneous backward method. Consequently, the SDEED problem is decomposed into a number of deterministic multiobjective dispatch problems. For each scenario, an enhanced multiobjective particle swarm optimization (EMOPSO) algorithm integrated with the handling strategy for system operation constraints is proposed to produce the Pareto optimal solutions. Furthermore, the available reserve capacity by the UC solution is verified. The simulation results demonstrate that the proposed probabilistic scenario–based and EMOPSO approach is practicable for solving SDEED-UC from the perspectives of system operation economy, emission, and reliability simultaneously.