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This paper presents a new methodology based on multiobjective optimization techniques to perform an optimized, coordinated, and selective allocation of control and protection devices in distribution networks with distributed generation (DG). The proposed mathematical model consists of two objective functions that consider economic issues and the network continuity index. Physical and operational constraints are taken into account, with emphasis on the set of constraints based on practical rules of distribution companies and international technical standards, which require the specification, coordination, and selectivity of the protection devices installed in the network. The possibility of load transfer from neighboring feeders and islanded DG operation is also considered. The proposed model is a mixed integer nonlinear programming, and we use NSGA-II (Nondominated Sorting Genetic Algorithm) to solve it. The proposed methodology is applied in a real 135-bus system found in the literature. [ABSTRACT FROM AUTHOR]

This paper investigates the multistage expansion planning problem of a distribution network considering reliability. Thus, the best alternative, location, and installation time for the candidate assets are identified while jointly accounting for economic and reliability aspects. As a major salient feature, the conventional simulation-based reliability assessment is equivalently implemented through algebraic expressions whereby the effect of the network topology is explicitly represented by decision variables of the optimization process. For expository purposes, the focus is placed on the expected energy not supplied, which is a widely-used metric for reliability assessment. The resulting optimization problem is cast as an instance of mixed-integer linear programming. Hence, unlike existing heuristic and metaheuristic solution techniques for reliability-constrained distribution system planning, the proposed approach is finitely convergent to the optimal solution and can be readily implemented using commercially available software. Simulation results show the effective performance of the proposed methodology. [ABSTRACT FROM PUBLISHER]

Decision making in the operation and planning of power systems is, in general, economically driven, especially in deregulated markets. To better understand the participants' behavior in power markets, it is necessary to include concepts of microeconomics and operations research in the analysis of power systems. Particularly, game theory equilibrium models have played an important role in shaping participants' behavior and their interactions. In recent years, bilevel games and their applications to power systems have received growing attention. Bilevel optimization models, Mathematical Program with Equilibrium Constraints and Equilibrium Problem with Equilibrium Constraints are examples of bilevel games. This paper provides an overview of the full range of formulations of non-cooperative bilevel games. Our aim is to present, in an unified manner, the theoretical foundations, classification and main techniques for solving bilevel games and their applications to power systems. [ABSTRACT FROM AUTHOR]

Due to the rapid growth in the amount of wind energy connected to distribution grids, they are exposed to higher network constraints, which poses additional challenges to system operation. Based on regulation, the system operator has the right to curtail wind energy in order to avoid any violation of system constraints. Energy storage systems (ESS) are considered to be a viable solution to solve this problem. The aim of this paper is to provide the best locations of both ESS and wind power by optimizing distribution system costs taking into account network constraints and the uncertainty associated to the nature of wind, load and price. To do that, we use a mixed integer linear programming (MILP) approach consisting of loss reduction, voltage improvement and minimization of generation costs. An alternative current (AC) linear optimal power flow (OPF), which employs binary variables to define the location of the generation, is implemented. The proposed stochastic MILP approach has been applied to the IEEE 69-bus distribution network and the results show the performance of the model under different values of installed capacities of ESS and wind power. [ABSTRACT FROM AUTHOR]

This paper addresses the multistage expansion planning problem of a distribution system where investments in the distribution network and in distributed generation are jointly considered. Network expansion comprises several alternatives for feeders and transformers. Analogously, the installation of distributed generation takes into account several alternatives for conventional and wind generators. Unlike what is customarily done, a set of candidate nodes for generator installation is considered. Thus, the optimal expansion plan identifies the best alternative, location, and installation time for the candidate assets. The model is driven by the minimization of the net present value of the total cost including the costs related to investment, maintenance, production, losses, and unserved energy. The costs of energy losses are modeled by a piecewise linear approximation. As another distinctive feature, radiality conditions are specifically tailored to accommodate the presence of distributed generation in order to avoid the isolation of distributed generators and the issues associated with transfer nodes. The resulting optimization problem is a mixed-integer linear program for which finite convergence to optimality is guaranteed and efficient off-the-shelf software is available. Numerical results illustrate the effective performance of the proposed approach. [ABSTRACT FROM AUTHOR]

Different regulatory frameworks have implemented competitive mechanisms to increase efficiency in transmission, a natural monopoly. Conflicts of interest and hidden costs make necessary to define methods to obtain an appropriate valuation of new transmission assets. [ABSTRACT FROM PUBLISHER]

Abstract: This paper presents an approach based on a specialized genetic algorithm (GA) to determine the location and contract pricing of dispatchable distributed generation (DG) units in distribution systems. The proposed approach is based on a nonlinear bilevel programming framework that involves the interests of two different agents: the DG owner who procures the maximization of the profits obtained from the energy sales, and the Distribution Company (DisCo), which procures the minimization of the payments incurred in attending the forecasted demand. To meet the forecasted demand the DisCo can purchase energy either from the wholesale energy market or from the DG units within its network. The proposed GA determines both the location and contract pricing of the DG units that would render maximum profits to the DG owner, subject to the minimization of payments procured by the DisCo. To show the effectiveness of the proposed approach, several tests were carried out on a modified IEEE 34-bus and 85-bus distribution networks. [Copyright &y& Elsevier]

This paper presents a model for solving the multistage planning problem of a distribution network. The objective function to be minimized is the net present value of the investment cost to add, reinforce or replace feeders and substations, losses cost, and operation and maintenance cost. The model considers three levels of load in each node and two investment alternatives for each resource to be added, reinforced or replaced. The nonlinear objective function is approximated by a piecewise linear function, resulting in a mixed integer linear model that is solved using standard mathematical programming. The model allows us to find multiple solutions in addition to the optimal one, helping the decision maker to analyze and choose from a pool of solutions. In addition to the optimization problem, reliability indexes and associated costs are computed for each solution, based on the regulation model used in Brazil. Numerical results and discussion are presented for an illustrative 27-node test network. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents an analysis and discussion, based on cooperative game theory, for the allocation of the cost of losses to generators and demands in transmission systems. We construct a cooperative game theory model in which the players are represented by equivalent bilateral exchanges and we search for a unique loss allocation solution, the Core. Other solution concepts, such as the Shapley Value, the Bilateral Shapley Value and the Kernel are also explored. Our main objective is to illustrate why is not possible to find an optimal solution for allocating the cost of losses to the users of a network. Results and relevant conclusions are presented for a 4-bus system and a 14-bus system. [Copyright &y& Elsevier]

This paper presents a mixed-integer linear programming (LP) formulation for the long-term transmission expansion planning problem in a competitive pool-based electricity market. To achieve optimal expansion planning while modeling market functioning, we define a number of scenarios based on the future demand in the system and we simulate the maximization of the aggregate social welfare. Investment and operating costs, transmission losses and generator offers, and demand bids are considered. We propose to use a set of metrics to rate the effect of the expansion on the generators, demands, and the system as a whole. The proposed model is applied to the Garver six-bus system and to the IEEE 24-bus Reliability Test System. Simulation results can be interpreted in economic terms based on the values of the metrics obtained for different scenarios, parameters, and topologies. [ABSTRACT FROM AUTHOR]

Discusses the application of the distributed optimization software tool DistOpt to the optimal power flow problem in distributed energy management systems. Effect of deregulation on the electric power industry; Considerations about the requirements for Energy Management System tools in a deregulated environment; Basis for the DistOpt software tool; Overview of power system optimization problems.

Studies the decentralized coalition scheme in a specific electric power systems transmission expansion scenario. Description of the network expansion model; Investigation of the transmission expansion process; Decentralized coalition formation between transmission expansion agents.

Energy hub (EH) is a concept that is commonly used to describe multi-carrier energy systems. New advances in the area of energy conversion and storage have resulted in the development of EHs. The efficiency and capability of power systems can be improved by using EHs. This paper proposes an Information Gap Decision Theory (IGDT)-based model for EH management, taking into account the demand response (DR). The proposed model is applied to a semi-realistic case study with large consumers within a day ahead of the scheduling time horizon. The EH has some inputs including real-time (RT) and day-ahead (DA) electricity market prices, wind turbine generation, and natural gas network data. It also has electricity and heat demands as part of the output. The management of the EH is investigated considering the uncertainty in RT electricity market prices and wind turbine generation. The decisions are robust against uncertainties using the IGDT method. DR is added to the decision-making process in order to increase the flexibility of the decisions made. The numerical results demonstrate that considering DR in the IGDT-based EH management system changes the decision-making process. The results of the IGDT and stochastic programming model have been shown for more comprehension. [ABSTRACT FROM AUTHOR]

This paper presents a new approach for long-term reactive power investment planning and operation using a multiperiod mixed-integer stochastic convex model, where load uncertainty is also included. The risk of not meeting the load with a certain level of confidence due to a reactive power deficit is represented by chance constraints. Tap settings of under-load tap-changing transformers are modeled as a set of mixed-integer linear equations. Existing and new discrete and continuous reactive power sources are modeled. These type of problem is challenging and have never been studied before. The proposed model is applied to the CIGRE-32 electric power system. [ABSTRACT FROM AUTHOR]

The use of electric vehicles is being promoted to address emerging concerns about global warming associated with emissions from fossil fuels. Besides, in the context of parcel delivery deep growth related to e-commerce, electric vehicle is becoming an alternative to conventional fossil fuel technology. Intrinsically, the charging process implies the interdependence between the transportation and electric power systems. This paper presents a new multistage optimization-based approach that allows linking delivery routing and aggregated demand management in the transportation and electric power systems, respectively. For the routing and charging of each independent electric vehicle, battery degradation, acceleration- and speed-dependent power consumption, penalty for delivery delay, tolls, fixed charging prices and incentives for availability time are considered. An electric vehicle demand aggregator is used to guarantee the synergy between systems. Incentives are included to motivate electric vehicles to remain at charging intersections. However, attractive incentives can create electric power system congestion due to simultaneous charges on nodes. Thus, an iterative decongestion methodology is developed. The resulting model is divided into three stages: delivery allocation, delivery routing for each independent electric vehicle and optimal energy management by electric vehicle demand aggregator. The resulting optimization problem is cast as a mixed-integer linear programming model for the first two stages and a linear programming model for the third stage. Numerical results demonstrate the effectiveness of the proposed model on a real 284-intersection map with a set of 100 electric vehicles, showing that incentives allow electric vehicle demand aggregator to achieve cost savings of 8.5%. [Display omitted] • Detailed features of electric vehicle operation and parcel delivery are considered. • The proposed model links delivery routing and aggregated energy management. • An electric vehicle demand aggregator guarantees the synergy between systems. • The model stages are: delivery allocation, delivery routing and energy management. • The incentives allow the electric vehicle aggregator to achieve cost savings of 8.5%. [ABSTRACT FROM AUTHOR]

Abstract: This work is devoted to study and discuss the main methods to solve the network cost allocation problem both for generators and demands. From the presented, compared and discussed methods, the first one is based on power injections, the second deals with proportional sharing factors, the third is based upon Equivalent Bilateral Exchanges, the fourth analyzes the power flow sensitivity in relation to the power injected, and the last one is based on network matrix. All the methods are initially illustrated using a 4-bus system. In addition, the IEEE 24-bus RTS system is presented for further comparisons and analysis. Appropriate conclusions are finally drawn. [Copyright &y& Elsevier]

Highlights: [•] The new approach to allocate transmission cost. [•] The approach is based on LRMC method and the min–max optimization technique. [•] The approach reduces the dispersion of the tariffs maintaining the locational signal. [•] An iterative method to set tariffs based on a linear programming optimization model. [Copyright &y& Elsevier]