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This paper develops an equilibrium-based model for ex ante analysis of capacity withholding under financial virtual divestiture in a day-ahead market, including one dominant producer and a number of small producers considered as a price-taking competitive fringe. We model the offering problem of the dominant producer as a bilevel optimization problem. In the upper-level problem, the dominant producer derives optimal strategy of physical capacity withholding restricted by operational limits of its own generating units and conditions stated in the financial virtual divestiture. In the lower-level problem, market operator clears the day-ahead market on an hourly basis. We recast the bilevel optimization problem as a mathematical programming with equilibrium constraints problem and convert it to a mixed integer linear programming problem. We show how the application of the financial virtual divestiture may mitigate market power in the day-ahead market, through a case study. The numerical results demonstrate that the dominant producer can exploit technological diversification of its own generating units to exercise market power, even in the presence of this market power mitigation policy. [ABSTRACT FROM PUBLISHER]

An optimal planning model of the sectional switch and tie line for the distribution network is proposed considering the tradeoff between the system reliability and economy. First, the fault incidence matrices are established for the distribution network, and the explicit analytical expression of the system reliability index is realized by the algebraic operation of the fault incidence matrices and fault parameter vectors. Then, the 0–1 integer quadratically constrained optimization model is established for the sectional switch configuration considering the capacity constraint of the tie line. Finally, the sectional switches and tie lines are optimized jointly to ensure the economic investment and maximize the system reliability. The distribution network in Taiwan Power Company is used to verify the applicability of the proposed optimization method. Compared with the intelligent optimization algorithm, the mathematical programming method in this paper can always find the global optimal switch configuration quickly. In addition, the influence of the tie line capacity constraints on the sectional switch configuration is also considered, which can provide practical reference for the power network planners in the optimization design of the distribution network. [ABSTRACT FROM AUTHOR]

Interconnection wide electric transmission system has been envisioned to facilitate the growth of renewable energy, enhance reliability, improve system operating efficiency, and reduce emissions. In this paper, we propose an explicit planning approach for transmission design at the national level. It has been applied to the U.S. system to design transmission overlays for multiple future generation scenarios. Associated simulation results suggest that a national transmission overlay provides economic, environmental, and system performance benefits. [ABSTRACT FROM PUBLISHER]

This paper provides a methodology to assist a strategic producer in making informed decisions on generation investment. A single target year is considered with demand variations modeled through blocks. The strategic behavior of the producer is represented through a bilevel model: the upper-level considers both investment decisions and strategic production actions and the lower-level corresponds to market clearing. Prices are obtained as dual variables of power balance equations. Rival uncertainties (on offering and investment) are characterized through scenarios. The resulting model is a large-scale mixed-integer LP problem solvable using currently available branch-and-cut techniques. Results pertaining to an illustrative example and a case study are reported and discussed. [ABSTRACT FROM PUBLISHER]

This paper addresses the problem of joint optimal allocation and sizing of dynamic Var sources (ODynVarS) for short-term dynamic performance enhancement of power systems. A heuristic optimization based approach is proposed with the aim of achieving a desirable performance under multiple contingencies with respect to a reference short-term voltage response. Based on a pre-selected set of credible contingencies and potential candidate locations for device additions, the approach employs the mean-variance mapping optimization (MVMO) in combination with an integrated mixed-integer search strategy (IMIS) and two intervention schemes, to accelerate and guide the search process towards a techno-economically optimum solution. The first intervention is performed at fitness evaluation stage to screen economic solutions which entail technical feasibility and to reduce computational effort. IMIS and the second intervention is concerned with MVMO's knowledge management, which facilitates the search for solutions comprising a small number of devices with relatively large VAr capacity. The approach is tested using the IEEE 39-bus New England system. [ABSTRACT FROM AUTHOR]

An effective means for analyzing the impact of novel operating schemes on power systems is time-domain simulation, for example, for investigating optimization-based curtailment of renewables to alleviate voltage violations. Traditionally, interior-point methods are used for solving the non-convex AC optimal power flow (OPF) problems arising in this type of simulation. This paper presents an alternative algorithm that better suits the simulation framework, because it can more effectively be warm started, has linear computational and memory complexity in the problem size per iteration and globally converges to Karush–Kuhn–Tucker (KKT) points with a linear rate if they exist. The algorithm exploits a difference-of-convex-functions reformulation of the OPF problem, which can be performed effectively. Numerical results are presented comparing the method to state-of-the-art OPF solver implementations in MATPOWER, leading to significant speedups compared to the latter for smaller systems and comparable performance for larger cases. [ABSTRACT FROM PUBLISHER]

This paper deals with day-ahead static security assessment with respect to a postulated set of contingencies while taking into account uncertainties about the next day system conditions. We propose a heuristic approach to compute the worst-case under operation uncertainty for a contingency with respect to overloads. We formulate this problem as a non-convex nonlinear bilevel program that we solve approximately by a heuristic approach which relies on the solution of successive optimal power flow (OPF) and security-constrained optimal power flow (SCOPF) problems of a special type. The method aims at revealing those combinations of uncertainties and contingencies for which the best combination of preventive and corrective actions would not suffice to ensure security. Extensive numerical results on a small, a medium, and a very large system prove the interest of the approach. [ABSTRACT FROM PUBLISHER]

This paper deals with day-ahead power systems security planning under uncertainties by posing an optimization problem over a set of power injection scenarios that could show up the next day and modeling the next day's real-time control strategies aiming at ensuring security with respect to contingencies by a combination of preventive and corrective controls. We seek to determine whether and which day-ahead decisions must be taken so that, for scenarios over the next day, there still exists an acceptable combination of preventive and corrective controls ensuring system security for any postulated contingency. We formulate this task as a three-stage feasibility checking problem, where the first stage corresponds to day-ahead decisions, the second stage to preventive control actions, and the third stage to corrective post-contingency controls. We propose a solution approach based on the problem decomposition into successive optimal power flow (OPF) and security-constrained optimal power flow (SCOPF) problems of a special type. Our approach is illustrated on the Nordic32 system and on a 1203-bus model of a real-life system. [ABSTRACT FROM PUBLISHER]

Solution approaches to chance constrained programming (CCP) have been recently developed and applied in many areas for optimization under uncertainty. Due to the nonlinear model with multiple uncertain variables as well as multiple output constraints, CCP has not been directly applied to optimal power flow (OPF) under uncertainty. The objective of this paper is twofold. First, we introduce the CCP approach to OPF under uncertainty and analyze the computational complexity of the chance constrained OPF. Second, the effectiveness of implementing a back-mapping approach and a linear approximation of the nonlinear model equations to solve the formulated CCP problem is investigated. Load power uncertainties are considered as multivariate random variables with correlated normal distribution. Based on both the nonlinear and the linearized model, results of a five-bus system and the IEEE 30-bus test system are presented to demonstrate the scope of chance constrained OPF. [ABSTRACT FROM PUBLISHER]

This paper proposes an active-reactive power dispatch procedure that is expressed via a bilevel optimization problem whose upper and lower level criteria are, respectively, the minimum opportunity cost and minimum offered price of active power. It analyzes the impact of minimizing such criteria on system operation, and it investigates the behavior of marginal prices and opportunity costs and their use in mechanisms to compensate generators for power provision. The bilevel problem is solved using a version of interior point methods derived for mathematical programs with complementarity constraints. Results obtained with three test systems are analyzed. [ABSTRACT FROM PUBLISHER]

Deregulation of the electric power industry has introduced new uncertainties for market participants and made planning of transmission expansion more difficult. More flexible transmission expansion plans are needed, to cope with the increased risks. In this paper, a novel planning approach is proposed to meet the above challenge. In our approach, the planning process is modeled as a mixed integer nonlinear programming (MINLP) problem, so that conflicting objectives can be optimized simultaneously. To minimize planning risks, our method identifies several scenarios based on statistics and expert knowledge; the most flexible expansion plan is selected as the plan that has least adaptation cost. The proposed method is tested with the IEEE 14-bus system. Promising results are obtained to demonstrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]

This paper presents a new method for VAr planning under multiple operating scenarios, optimizing sizes and locations of new reactive compensation equipment to ensure that both the system voltage profile and voltage stability requirements are met. The approach is based on \mbi L1-norm regularization for finding a solution with minimum VAr installation sites and on the \mbi L2-norm penalty function for satisfying the multiple state constraints. The \mbi L2-norm penalty function is exact in the sense that a finite penalty parameter is required to establish equivalence with the VAr planning problem, thus avoiding numerical ill-conditioning. The solution is obtained from a successive conic programming algorithm which makes use of adaptive trust-region control. The results of the new method are compared with those of an optimal power flow-based program for VAr planning. [ABSTRACT FROM PUBLISHER]

The controllable capacitive reactance can be used as the input variable for the external controller of a series capacitive reactance compensator (SCRC) to improve the damping of low, frequency oscillations of the rotor angle and active power in a power system. Conventional linear PI controllers are tuned for best performance at one specific operating point of the nonlinear power system. At other operating point its performance degrades. Nonlinear optimal neuro-controllers are able to overcome this degradation. In this paper, the dual heuristic dynamic programming (PUP) optimization algorithm is applied to design an external nonlinear optimal neuro-controller for the SCRC. Simulation studies using PSCAD/EMTDC® software are presented. [ABSTRACT FROM AUTHOR]

Formulating the alternating current optimal power flow (ACOPF) as a polynomial optimization problem makes it possible to solve large instances in practice and to guarantee asymptotic convergence in theory. We formulate the ACOPF as a degree-two polynomial program and study two approaches to solving it via convexifications. In the first approach, we tighten the first-order relaxation of the nonconvex quadratic program by adding valid inequalities. In the second approach, we exploit the structure of the polynomial program by using a sparse variant of Lasserre's hierarchy. This allows us to solve instances of up to 39 buses to global optimality and to provide strong bounds for the Polish network within an hour. [ABSTRACT FROM AUTHOR]

Several pure binary integer optimization models are developed for clustering time periods by similarity for electricity utilities seeking assistance with pricing strategies. The models include alternative objectives for characterizing various notions of within-cluster distances, admit as feasible only clusters that are contiguous, and allow for circularity, where time periods at the beginning and end of the planning cycle may be in the same cluster. Restrictions upon cluster size may conveniently be included without the need of additional constraints. The models are populated with a real-world dataset of electricity usage for 93 buildings and solutions and run-times attained by conventional optimization software are compared with those by dynamic programming, or by a greedy algorithm applicable to one of the models, that run in polynomial time. The results provide time-of-use segments that an electricity utility may employ for selective pricing for peak and off-peak time periods to influence demand for the purpose of load leveling. [ABSTRACT FROM PUBLISHER]

We address the generation investment problem faced by a strategic power producer and consider a detailed description of the uncertain parameters involved, namely, rival producer investment and market offering, and demand growth. To identify optimal investment decisions, we consider a target year and propose a bilevel model whose upper-level problem determines investment and offering decisions to maximize expected profit, and whose many lower-level problems represent market clearing conditions per demand block and scenario. Since the producer total expected profit is sufficiently convex with respect to investment decisions, a Benders' decomposition approach is proposed that results in a tractable formulation even if hundred of scenarios are used to describe uncertain parameters. Extensive numerical simulations based on realistic case studies show the good performance of the proposed decomposition approach. [ABSTRACT FROM PUBLISHER]

This work describes an economic analysis of the inclusion of pumped storage in a small island system that has abundant renewable energy available but that at times cannot accept all of this power because of limits imposed by security criteria. The question of whether or how much pumped storage to include is addressed by formulating a linear programming optimization problem. The stochastic nature of load and renewable production is addressed using scenarios developed through fuzzy clustering. Both the unit capacity in MW and the reservoir storage capacity in MWh are optimized, and optimal operating strategies for the scenarios are produced. Results showed that including pumped storage can be an effective means of allowing larger penetration of intermittent renewable energy sources, improving both the dynamic security and the economic operation of a test system. Including the dynamic security criteria in the economic question of dimensioning the pumped storage unit proved to make a significant difference in the optimal pumped storage capacity. [ABSTRACT FROM AUTHOR]

This letter presents a stochastic robust framework for two-stage power system optimization problems with uncertainty. The model optimizes the probabilistic expectation of different worst-case scenarios with different uncertainty sets. A case study of unit commitment shows the effectiveness of the proposed model and algorithms. [ABSTRACT FROM AUTHOR]

This letter proposes an absolute value based method to solve the interval linear optimization problem with application to security constrained economic dispatch (SCED). To avoid expensive computation associated with solving combinatorial linear programming (LP) problems for interval upper bound, firstly a bilinear programming model is formulated using duality theory. The equality constraints are then converted to absolute value constraints. Lastly, the absolute value operator is eliminated through introducing two nonnegative slack variables and complementary slackness condition. The resulting new bilinear programming model can be effectively solved by the branch and bound method with linear relaxation technique to obtain the global optimal solution. Numerical results demonstrate the effectiveness of the proposed method in improving solution and computation time. [ABSTRACT FROM AUTHOR]