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The aggressive goal of having a 100% renewable energy system requires preparing an appropriate infrastructure for deploying as much distributed generation (DG) as possible into the electricity network. This article aims at proposing a new framework to maximize the hosting capacity (HC) of DGs in a distribution network. This operation-planning framework provides a suitable and interactive environment for the electricity market, distribution company (DISCO), electric vehicle (EV) aggregator, and rival DISCOs to increase the HC. The problem is modeled via a bilevel conditional-value-at-risk-constrained stochastic programming approach. The DISCO, in the upper level (UL), tries to maximize the HC and minimize the operating costs while interacting with a passive electricity market and an EV aggregator. The aggregator, in the lower level, aims to maximize their profit by interacting with the primary DISCO in the UL and the rival DISCO, to satisfy the EV owners. The Karush–Kuhn–Tucker conditions are used to recast the model into an equivalent single level. The proposed framework is tested on a 33-node distribution network. Results show how an interactive-based framework can contribute to maximizing the HC of DGs. [ABSTRACT FROM AUTHOR]

Reliability is an essential factor in distribution networkt expansion planning. However, standard distribution reliability assessment techniques rely on quantifying the impact of a pre-specified set of events on service continuity through the simulation of component outages, one at a time. Due to such a simulation-based nature, the incorporation of reliability into distribution network expansion planning has customarily required the application of heuristic and metaheuristic approaches. Recently, alternative mixed-integer linear programming (MILP) models have been proposed for distribution network expansion planning considering reliability. Nonetheless, such models suffer from either low computational efficiency or over-simplification. To overcome these shortcomings, this paper proposes an enhanced MILP model for multistage reliability-constrained distribution network expansion planning. Leveraging an efficient, yet accurate reliability evaluation model, proposing a customized technique for effectively imposing radial operation, as well as utilizing pragmatic measures to model reliability-related costs are the salient features of this work. In this respect, practical reliability-related costs are considered based on reliability incentive schemes and the revenue lost due to undelivered energy during customer outages. The proposed planning approach is tested on four networks with 24, 54, 86, and 138 nodes to illustrate its efficiency and applicability. [ABSTRACT FROM AUTHOR]

The increased deployment of distributed generation calls for the coordination and interaction between the transmission and distribution levels. This requirement is particularly relevant for planning purposes when renewable-based generation is involved. Unfortunately, in current industry practice, transmission and distribution network planners solve their problems independent of each other, thereby leading to suboptimal solutions. Within this context, this paper addresses the integrated expansion planning problem of transmission and distribution systems where investments in network and generation assets are jointly considered. Several alternatives are available for the installation of lines as well as conventional and renewable-based generators at both system levels. Thus, the optimal expansion plan identifies the best alternative for the candidate assets under the uncertainty associated with demand and renewable-based power production. The proposed model is an instance of stochastic programming wherein uncertainty is characterized through a set of scenarios that explicitly capture the correlation between the uncertain parameters. The resulting stochastic program is driven by the minimization of the expected total cost, which comprises the costs related to investment decisions and system operation. The associated scenario-based deterministic equivalent is formulated as a mixed-integer linear program for which finite convergence to optimality is guaranteed. Numerical results show the effective performance of the proposed approach. [ABSTRACT FROM AUTHOR]

Continuity of supply plays a significant role in modern distribution system planning and operational studies. Accordingly, various techniques have been developed for reliability assessment of distribution networks. However, owing to the complexities and restrictions of these methods, many researchers have resorted to several heuristic optimization algorithms for solving reliability-constrained optimization problems. Therefore, solution quality and convergence to global optimality cannot be guaranteed. Aiming to address this issue, two salient mathematical models are introduced in this paper for topology-variable-based reliability evaluation of both radial and radially-operated meshed distribution networks. Cast as a set of linear expressions, the first model is suitable for radial networks. The second model relies on mixed-integer linear programming and allows handling not only radial networks but also radially-operated meshed distribution grids. Therefore, the proposed formulations can be readily incorporated into various mathematical programming models for distribution system planning and operation. Numerical results from several case studies back the scalability of the developed models, which is promising for their further application in distribution system optimization studies. Moreover, the benefits of the proposed formulations in terms of solution quality are empirically evidenced. [ABSTRACT FROM AUTHOR]

This paper addresses the multistage or dynamic investment decision-making problem arising in a distribution system within a transactive energy environment. Under such a framework, three agents are involved, namely the distribution company (DISCO), the owner of distributed generation (DG) units (DGENCO), and the independent distribution system operator (IDSO). Here, we propose characterizing this planning problem as an instance of trilevel programming. In the upper level, the DISCO identifies the optimal investment plan in network assets and the best potential locations for DG in order to supply the customer demand at maximum profit. In the middle level, the DGENCO determines the best location, sizing, and timing for DG installation so that the corresponding profit is maximized. Finally, in the lower level, the IDSO is responsible for the optimal operation of the expanded distribution system. The resulting mixed-integer trilevel program is solved by a novel approach relying on Benders decomposition. The performance of the proposed approach has been illustrated with a case study based on a 37-node test system. [ABSTRACT FROM AUTHOR]

Energy storage systems (ESS) have adopted a new role with the increasing penetration of electric vehicles (EV) and renewable energy sources (RES). EV introduce new charging demands that change the traditional demand profiles and RES are characterized by their high variability. This paper presents a new multistage distribution expansion planning model where investments in distribution network assets, RES, ESS, and EV charging stations are jointly considered. The charging demand necessary for EV transportation is performed using a vehicle model based on travel patterns. The variability associated with RES along with the demand requires the incorporation of uncertainty, which is characterized through a set of scenarios. These scenarios are generated by the ${k}$ -means++ clustering technique that allows keeping the correlation in the information of the uncertainty sources. The resulting stochastic program is driven by the minimization of the present value of the total expected cost including investment, maintenance, production, losses, and non-supplied energy. The associated scenario-based deterministic equivalent is formulated as a mixed-integer linear program, which can be solved by commercial software. Numerical results are presented for an illustrative 54-node test system. [ABSTRACT FROM AUTHOR]

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]

The advent of smart grids and active distribution networks has boosted the relevance of reliability in the operation and planning of distribution systems. As is customary, reliability is assessed analytically through several standard indices. Unfortunately, analytical reliability assessment relies on simulation, thereby requiring the use of inexact heuristic- or metaheuristic-based solution methods to operate and plan distribution systems when economic and reliability criteria are jointly considered. In order to overcome this shortcoming, this paper presents a new optimization-based approach to compute the standard network-dependent reliability indices that are widely used in reliability-constrained distribution optimization models. As a major salient feature over the conventional simulation-based method, reliability indices are equivalently determined by an efficient approach based on linear programming, where the network topology is explicitly represented by decision variables of the optimization process. The proposed approach has been tested on several benchmarks including a 1080-node system. Numerical simulations show that the proposed approach yields the same results as the conventional algorithm. Moreover, the moderate computational effort is suitable for the subsequent integration of the proposed equivalent formulation in reliability-constrained optimization models for distribution operation and planning. Such successful numerical experience backs the potential of the proposed formulation to enable the use of sound techniques different from the available heuristics and metaheuristics to solve reliability-constrained operational and planning optimization models for distribution systems. [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]

The first part of this two-paper series describes the incorporation of demand response (DR) and energy storage systems (ESSs) in the joint distribution and generation expansion planning for isolated systems. The role of DR and ESS has recently attracted an increasing interest in power systems. However, previous models have not been completely adapted in order to treat DR and ESS on an equal footing. The model presented includes DR and ESS in the planning of insular distribution systems. Hence, this paper presents a novel model to decide the joint expansion planning of distributed generation and the distribution network considering the impact of ESS and price-dependent DR programs. The problem is formulated as a stochastic-programming-based model driven by the maximization of the net social benefit. The associated deterministic equivalent is formulated as a mixed-integer linear program suitable for commercially available software. The outcomes of the model are the location and size of new generation and storage units and the distribution assets to be installed, reinforced or replaced. In the second companion paper, an insular case study (La Graciosa, Canary Islands, Spain) is provided illustrating the effects of DR and ESS on social welfare. [ABSTRACT FROM PUBLISHER]

The second part of this two-paper series analyzes the incorporation of demand response (DR) and energy storage systems (ESSs) in the joint distribution and generation expansion planning for the isolated system of La Graciosa, Canary Islands, Spain. Based on the stochastic programming model developed in its companion paper, the impact of DR and ESS on the location and size of new generation and storage units and the distribution assets to be installed, reinforced, or replaced are defined. Numerical results illustrate the effective performance of the proposed approach. Additionally, a set of metrics showing the welfare achieved by the different stakeholders has been implemented. [ABSTRACT FROM PUBLISHER]

This paper presents a bi-level model for distribution network and renewable energy expansion planning under a demand response (DR) framework. The role of DR has recently attracted an increasing interest in power systems. However, previous models have not been completely adapted in order to treat DR on an equal footing. The target of the distribution network and generation planner, modeled through the upper-level problem, is to minimize generation and network investment cost while meeting the demand. This upper-level problem is constrained by the lower-level problem, stressing the importance of integrating DR to time-varying prices into those investment models. The objective function considered for the lower level is the minimization of overall payment faced by the consumers. Using the Karush-Kuhn-Tucker complementarity constraints, the proposed bi-level model is recast as a mixed-integer linear programming problem, which is solvable using efficient off-the-shelf branch-and-cut solvers. Detailed results from an insular case study (La Graciosa, Canary Islands, Spain) are presented. [ABSTRACT FROM PUBLISHER]

The implementation of a centralized transmission expansion plan is a complex task when several investors compete and bid to build a new transmission asset, as is the case now in Chile. The assessment of a transmission project depends on the number of competitors, where the project is subject to risks, such as delays, penalties, and cost overruns. The risk faced by an investor is measured using the Conditional Value at Risk (CVaR), which can be interpreted as the risk of not reaching an expected return on investment, depending on the tolerance to risk and the real income obtained with the investor's portfolio. This risk comes from the difference between the regulated income (investor's bid) and the real cost during the implementation and operation of the transmission project. The difference is the “surplus” profit that the investor obtains by participating in the tender, considering their risk tolerance. The goal is to determine the optimal value of a risky investor's portfolio made up of several transmission projects. The optimal portfolio may allow the central planner to improve the efficiency of the project allocation process. To test the methodology, two case studies are analyzed: the IEEE 24-bus Reliability Test System and a predefined expansion plan of Chile's Central Interconnected System (SIC). [ABSTRACT FROM PUBLISHER]

This paper proposes a risk-based mixed integer quadratically-constrained programming model for the long-term VAr planning problem. Risk aversion is included in the proposed model by means of regret-based optimization to quantify the load shedding risk because of a reactive power deficit. The expected operation and expansion costs of new installed reactive power sources and load shedding risk are jointly minimized. Uncertainty in the active and reactive load demands has been included in the model. An $\epsilon$ -constraint approach is used to characterize the optimal efficient frontier. Also, discrete tap settings of tap-changing transformers are modeled as a set of mixed integer linear equations which are embedded into an ac optimal convex power flow. Computational results are obtained from a realistic South and South–East Brazilian power system to illustrate the proposed methodology. Finally, conclusions are duly drawn. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive sensitivity analysis to identify the uncertain parameters which significantly influence the decision-making process in distributed generation (DG) investments and quantify their degree of influence. To perform the analysis, a DG investment planning model is formulated as a novel multistage and multiscenario optimization problem. Moreover, to ensure tractability and make use of exact solution methods, the entire problem is kept as a mixed-integer linear programming optimization. A real-world distribution network system is used to carry out the analysis. The results of the analysis generally show that uncertainty as well as operational variability of the considered parameters have meaningful impacts on investment decisions of DG. The degree of influence varies from one parameter to another. But, in general, ignoring or inadequately considering uncertainty and variability in model parameters has a quantifiable cost. Hence, the analysis made in this paper can be very useful to identify the most relevant model parameters that need special attention in planning practices. [ABSTRACT FROM PUBLISHER]

This paper presents a novel multi-stage stochastic distributed generation investment planning model for making investment decisions under uncertainty. The problem, formulated from a coordinated system planning viewpoint, simultaneously minimizes the net present value of costs rated to losses, emission, operation, and maintenance, as well as the cost of unserved energy. The formulation is anchored on a two-period planning horizon, each having multiple stages. The first period is a short-term horizon in which robust decisions are pursued in the face of uncertainty; whereas, the second one spans over a medium to long-term horizon involving exploratory and/or flexible investment decisions. The operational variability and uncertainty introduced by intermittent generation sources, electricity demand, emission prices, demand growth, and others are accounted for via probabilistic and stochastic methods, respectively. Metrics such as cost of ignoring uncertainty and value of perfect information are used to clearly demonstrate the benefits of the proposed stochastic model. A real-life distribution network system is used as a case study and the results show the effectiveness of the proposed model. [ABSTRACT FROM PUBLISHER]

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]

This paper presents a model for long-term reactive power planning where a deterministic nonlinear model is expanded into a multi-stage stochastic model under load uncertainty and an N-k contingency analysis. Reactive load shedding is introduced in the objective function to measure the reactive power deficit after the planning process. The objective is to minimize the sum of investment costs (IC), expected operation costs (EOC) and reactive load shedding costs optimizing the sizes and locations of new reactive compensation equipment to ensure power system security in each stage along the planning horizon. An efficient scenario generation and reduction methodology is used for modeling uncertainty. Expected benefits are calculated to establish the performance of the expected value with perfect information (EVPI) and the value of the stochastic solution (VSS) methodologies. The efficacy of the proposed model is tested and justified by the simulation results using the Ward-Hale 6-bus and the IEEE 14-bus systems. [ABSTRACT FROM PUBLISHER]

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]

We present a three-level equilibrium model for the expansion of an electric network. The lower-level model represents the equilibrium of a pool-based market; the intermediate level represents the Nash equilibrium in generation capacity expansion, taking into account the outcomes on the spot market; and the upper-level model represents the anticipation of transmission expansion planning to the investment in generation capacity and the pool-based market equilibrium. The demand has been considered as exogenous and locational marginal prices are obtained as endogenous variables of the model. The three-level model is formulated as a mixed integer linear programming (MILP) problem. The model is applied to a realistic power system in Chile to illustrate the methodology and proper conclusions are reached. [ABSTRACT FROM PUBLISHER]

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]

We construct a general analytic framework for the transmission network investment problem in the market environment and demonstrate its application to some test systems. We define a set of metrics to quantify the improvement attained in terms of welfare for all the participants and make use of them in the evaluation of the impacts of new transmission investments under competition. The proposed metrics are useful to the central entity responsible for transmission planning to provide meaningful measures of the effects of a modification in the grid over the planning horizon. The proposed framework is particularly useful to transmission network planners to support desired environmental targets. The analysis of the effects of new investments on the participants affected by the expansion includes the assessment of appropriate environmental attributes. The consistency of the measured values in terms of these metrics allows the comparison of disparate transmission investment projects and their effective prioritization. A key element of the framework is the deployment of an optimization scheme to maximize the social welfare with and without the transmission asset investments under various bidding behaviors of the market players and contracting conditions. We report the application of the proposed framework to investigate several transmission expansion scenarios on the IEEE 24-bus reliability test system network. The results on both pool-based markets and combined pool-bilateral contract markets provide a good illustration of the capability of the framework to effectively address realistic questions in transmission investment. [ABSTRACT FROM AUTHOR]

Reserve and flexibility requirements are becoming more and more important for the optimal operation and planning of power systems to address the variability and uncertainty associated with increasing renewable generation. The objective of this special section is to cover this promising and dynamic area of research and development, while focusing on computational, technological, and regulatory aspects. [ABSTRACT FROM PUBLISHER]