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This paper proposes a Transient Stability Constrained Optimal Power Flow (TSCOPF) formulation that models non-synchronous renewable generation equipped with synthetic inertia. The proposed optimization problem calculates the optimal operating point of the system, accommodating high shares of non-synchronous renewable generation while ensuring transient stability in the event of critical incidents. Synthetic inertia controllers are used to improve the dynamic stability of the system in cases of very high share of renewable generation. The proposed tool is tested in the North-West Spanish system, a network with a high penetration of wind energy that causes a reduction in the total system inertia. The results of the study show that 1) synthetic inertia in renewable power plants can diminish electromechanical oscillations after a severe contingency, reducing the cost of ensuring transient stability; 2) using synthetic inertia the system becomes more stable when conventional generation is decommissioned following de-carbonization and renewable promotion policies; and 3) the proposed model can be used to calculate the parameters of the synthetic inertia control. [ABSTRACT FROM AUTHOR]

Despite major advancements in nonlinear programming (NLP) and convex relaxations, most system operators around the world still predominantly use some form of linear programming (LP) approximation of the AC power flow equations. This is largely due to LP technology's superior reliability and computational efficiency, especially in real-time market applications, security-constrained applications, and extensions involving integer variables, in addition to its ability to readily generate locational marginal prices (LMP) for market applications. In the aim of leveraging the advantages of LP while retaining the accuracy of NLP interior-point methods (IPMs), this paper proposes a sequential linear programming (SLP) approach consisting of a sequence of carefully constructed supporting hyperplanes and halfspaces. The algorithm is numerically demonstrated to converge on 138 test cases with up the 3375 buses to feasible high-quality solutions (i) without AC feasibility restoration (i.e., using LP solvers exclusively), (ii) in computation times generally within the same order of magnitude as those from a state-of-the-art NLP solver, and (iii) with robustness against the choice of starting point. In particular, the (relative) optimality gaps and the mean constraint violations are on average around $10^{-3}$ % and $10^{-7}$ , respectively, under a single parameter setting for all the 138 test cases. To the best of our knowledge, the proposed SLP approach is the first to use LP exclusively to reach feasible and high-quality solutions to the nonconvex AC OPF in a reliable way, which paves the way for system and market operators to keep using their LP solvers but now with the ability to accurately capture transmission losses, price reactive power (Q-LMP), and obtain more accurate LMP. [ABSTRACT FROM AUTHOR]

Since modern cyber-physical power systems are vulnerable to coordinated wide-area cyber attacks, it is necessary to mitigate the potential risk as much as possible. At the planning stage, the defender can utilize software diversity, which is a common phenomenon that the cyber software of different substations comes from different competing vendors. Therefore, different kinds of software may not be exposed to the same zero-day security loophole, preventing the attacker from taking charge of multiple substations at the same time. In this paper, the optimal scheme of software deployment considering long-term risk mitigation is studied. Firstly, the framework of diversity-based cyber defense against malicious attacks is formulated. Secondly, the risk index based on representative attack patterns is constructed, which is the objective to be minimized. Thirdly, considering that the deployment scheme is long-term stable while the operating mode varies with time, we construct a multiobjective nonlinear stochastic programming to mitigate the average risk of operating modes. Then the optimization problem is solved by the multiobjective genetic algorithm. Lastly, results of the IEEE 39-node CPPS and and the Virtual European Grid demonstrate that the proposed method can considerably reduce the attack risk. [ABSTRACT FROM AUTHOR]

To design a reliable and secure power system, it is necessary to have enough transmission capacity. The solution of transmission expansion planning (TEP) problem determines cost-optimal investment in future transmission equipment. In this paper, we propose a new global solver, named Global-TEP, for the TEP problem with an AC network representation (ACTEP), which is a mixed-integer nonlinear programming problem. The proposed solver is based on second-order cone relaxation, enhanced relaxation tightening constraints, and optimization-based/feasibility-based bound tightening techniques. Multiple enhanced relaxation tightening constraints are incorporated into the mixed-integer second-order cone relaxation of TEP in order to obtain a very strong relaxation as the lower bounding problem. In addition, a novel feasibility-based bound tightening technique is proposed to tighten the bounds of decision variables in a considerably short runtime. Finally, introducing a novel application of optimization-based bound tightening technique, Global-TEP is constructed that can solve the ACTEP problem efficiently with a guaranteed optimality gap. As illustrated by numerical case studies, Global-TEP is more scalable, more flexible, and much faster than the available global solvers. [ABSTRACT FROM AUTHOR]

Line-switching operations introduce several concerns to microgrid operators since they trigger reliability and power quality issues in addition to increasing the wear and tear cost of switching devices. This paper proposes a novel methodology for achieving seamless line-switching in low-inertia islanded microgrids. The proposed methodology aims at mitigating the detrimental line-switching impacts by minimizing the power flow in the switched lines at the moments of executing the switching operations. The minimization of the power flow is realized by optimizing the droop control parameters of the DGs during the transition from one network topology to another. Besides, during executing a series of line-switching operations, the sequence of switching is also optimized to minimize the total switched power. While optimizing the line-switching execution sequence, the transitional droops are optimized for each line-switching operation within that sequence. A dynamic mixed-integer nonlinear programming problem is formulated to find both the optimized dynamic transitional droops and line-switching sequence simultaneously. Simulation results demonstrate the effectiveness of the proposed seamless line-switching methodology where the switched power can be reduced by more than 80% for a single line-switching operation and 54% considering a series of four switch exchanges in addition to achieving several instances of zero-current switching. As a result, a significant decrease in the peak of voltage transients has been recorded for some cases. [ABSTRACT FROM AUTHOR]

The nonlinear programming (NLP) problem to solve distribution-level optimal power flow (D-OPF) poses convergence issues and does not scale well for unbalanced distribution systems. The existing scalable D-OPF algorithms either use approximations that are not valid for an unbalanced power distribution system, or apply relaxation techniques to the nonlinear power flow equations that do not guarantee a feasible power flow solution. In this paper, we propose scalable D-OPF algorithms that simultaneously achieve optimal and feasible solutions by solving multiple iterations of approximate, or relaxed, D-OPF subproblems of low complexity. The first algorithm is based on a successive linear approximation of the nonlinear power flow equations around the current operating point, where the D-OPF solution is obtained by solving multiple iterations of a linear programming (LP) problem. The second algorithm is based on the relaxation of the nonlinear power flow equations as conic constraints together with directional constraints, which achieves optimal and feasible solutions over multiple iterations of a second-order cone programming (SOCP) problem. It is demonstrated that the proposed algorithms are able to reach an optimal and feasible solution while significantly reducing the computation time as compared to an equivalent NLP D-OPF model for the same distribution system. [ABSTRACT FROM AUTHOR]

This paper tackles key industry needs in the scope of the AC Security-Constrained Optimal Power Flow (SCOPF) framework, specifically two major challenging requirements regarding generators’ post-contingency behavior: the (droop-based) frequency response and the voltage control mechanism (including PV/PQ switching). To this end, and to obtain a sufficiently accurate solution with a reasonable computational burden, we propose an approximation approach to SCOPF which extends significantly an existing algorithm with novel modelling elements: a continuous formulation to model generators’ post-contingency responses based on successive approximations, a technique to evaluate the viability of contingencies with respect to the base case set-points, and the complexity reduction of all post-contingency states through network compression and power flow piecewise linearization. All new features are validated both separately and in unison, to monitor the improvement of the solution time and the potential degradation of the solution's quality. The proposed approach is successfully applied to three power system network models, the largest of which consists of 2,000 buses and more than 3,000 contingencies. Finally, the trade-off between the limitations in terms of solution quality and the benefits in terms of computational effort are assessed for all proposed approximations. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of optimizing voltage set points and switching status of components in direct current power networks subject to physical and security constraints. The problem is cast as a mixed-integer nonlinear programming with two sources of computational complexity: i) Non-convex power flow equations, and ii) The presence of binary variables accounting for the on/off status of network components. A strengthened second-order cone programming (SOCP) relaxation is developed to tackle the non-convexity of power flow equations, and a branch-and-bound search is employed for determining optimal network configurations. The efficacy of the proposed method in optimizing the operation while mitigating contingencies is experimentally validated in a real-time hardware-in-the-loop environment using IEEE benchmark data. [ABSTRACT FROM AUTHOR]

We propose a transmission expansion planning model that integrates thyristor-controlled series compensators (TCSCs) to enhance line transmission capacity, and superconducting fault current limiters (SFCLs) to control short-circuit levels. The harmonious interplay between TCSCs and SFCLs results in effective and economically attractive optimal expansion plans. This multi-stage planning model translates into a complex mixed-integer nonlinear programming problem, which is hard to solve. To solve it, we propose a successive linearization technique within a Benders’ decomposition scheme that proves effective in finding optimal solutions and efficient in terms of computational burden. We illustrate the methodology proposed using the IEEE 39-bus system. [ABSTRACT FROM AUTHOR]

The integration of volatile distributed energy resources (DERs) brings new challenges for the active distribution network maintenance scheduling (DN-MS). Conventionally, the DN-MS is formulated as a deterministic optimization model without considering the uncertainties of DERs. In this paper, the DN-MS is formulated as a multistage stochastic optimization problem, which is cast as a stochastic mixed-integer nonlinear programming model. It aims to reduce the total maintenance cost constrained by the reliability indices. To capture the operational characteristics of active distribution networks, the uncertainties of DERs and post-outage operation strategies of switching devices are incorporated into the model. In general, this type of model is intractable and mainly solved by heuristic search methods with low efficiency. Recently, Monte-Carlo tree search (MCTS) is emerging as a scalable and promising reinforcement learning approach. We propose a stochastic MCTS solution to this problem. In the tree search procedure, a sample average approximation technique is developed to estimate multistage maintenance costs considering uncertainties. To speed up the MCTS, the complicated constraints of the original problem are transformed to penalty or heuristics functions. This approach can asymptotically approximate the optimum with promising computation efficiency. Numerical test results demonstrate the superiority of the proposed method over benchmark methods. [ABSTRACT FROM AUTHOR]

This paper proposes a trilateral bilevel stochastic mixed integer bilevel linear programming (MIBLP) model to handle a joint master-slave operation-planning problem. This model considers the interactions among an integrated community energy system (ICES), prosumers, and the wholesale electricity market (WM). The ICES, in the upper level, makes a joint optimal photovoltaic (PV) and energy storage system plan taking into account the concurrent interactions with the WM and prosumers to maximize its profit. On the other hand, in the lower level, the prosumers aim at minimizing their bills via an optimal PV-package sizing while interacting with the ICES and the WM simultaneously. The strong duality theorem is used to recast this MIBLP model into an equivalent single-level model. Since the lower level is also an operation-planning problem, this recast results in a mixed integer nonlinear programming (MINLP) model that prevents finding a satisfactory solution. To remedy this issue, sensitivity analysis and a separation-based linearization technique are applied. Numerical results illustrate the effective performance of the proposed business model while providing valuable information for the ICES and consumers. [ABSTRACT FROM AUTHOR]

The AC optimal power flow problem is a non-convex optimization problem that is difficult to solve quickly and reliably for large-scale grids. While various approximations have been proposed, they may lead to physically meaningless solutions. This paper presents a computationally efficient algorithm for constructing accurate linear approximations of line flow constraints. These approximations reduce the complexity of the optimization problem while ensuring that the solution is physically meaningful and has a high quality. The algorithm is based on an in-depth analysis of the feasible set of the line flow constraint. Numerical experiments are performed on ten large-scale systems using three nonlinear programming solvers. Obtained results indicate that the proposed formulation helps improve the solvers’ reliability and reduce the computation time for hard large-scale problems. At the same time, the developed algorithm provides high quality approximations of the line flow constraints. [ABSTRACT FROM AUTHOR]

This paper discusses the application of the recently proposed augmented Lagrangian alternating direction inexact Newton (aladin) method to non-convex ac optimal power flow problems (ac-opf) in a distributed fashion. In contrast to the often used alternating direction of multipliers method (admm), aladin guarantees locally quadratic convergence for ac-opf. Numerical results for 5–300 bus test cases indicate that aladin is able to outperform the admm and to reduce the number of iterations by about one order of magnitude. We compare aladin to numerical results for the admm documented in the literature. The improved convergence speed comes at the cost of increasing the communication effort per iteration. Therefore, we propose a variant of aladin that uses inexact Hessians to reduce communication. Additionally, we provide a detailed comparison of these aladin variants to the admm from an algorithmic and communication perspective. Moreover, we prove that aladin converges locally at a quadratic rate even for the relevant case of suboptimally solved local nonlinear programs. [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]

This paper discusses the marginal line loss approximation in the dc optimal power flow (DCOPF) and proposes a new methodology that improves the accuracy of current practices. In practice, the DCOPF solution may have significant differences compared to the ac base point that determines marginal losses, and this paper provides the only published example that illustrates this problem to our knowledge. The proposed methodology updates the marginal losses without solving a new ac base point so that the optimal solution accurately reflects system losses. Previously proposed methodologies require nonlinear programming solvers, additional ac power flow solutions, or are less accurate compared to actual losses, and they only give results for one or two test networks. This paper provides results on nine different test networks. The key advantage of the proposed methodology is that it can be easily integrated into current market software. [ABSTRACT FROM AUTHOR]

The problem of designing customized pricing strategies for different residential users is investigated based on the identification results of residential electric appliances and classifications of end-users according to their consumption behaviors. This study is based on the following assumptions: 1) Each retailer purchases electricity from the forward contract market, day-ahead spot market, and real-time market; 2) the competition among retailers is modeled by a market share function; 3) each retailer adopts fixed time-of-use prices for end-users; 4) the price fluctuations in day-ahead and real-time spot markets as well as uncertainty of electricity consumption behaviors are considered as main sources of risk. Under these assumptions, a pricing framework for retailers is established based on the bilevel programming framework and the optimal clustering in a time sequence. Meanwhile, profit risk is considered by taking conditional value at risk as the risk measure. The proposed bilevel optimization model is finally reformulated into a mixed-integer nonlinear programming problem by solving Karush–Kuhn–Tucker conditions. The online optimization solvers provided by the network-enabled optimization system server and the commercial solver AMPL/GUROBI are used to solve the developed models, respectively. Finally, a case study is employed to demonstrate the feasibility and efficiency of the developed models and algorithms. [ABSTRACT FROM PUBLISHER]

In this paper, we study the optimal location planning of renewable distributed generation (RDG) units by taking into account the random uncertainties of renewable generation and load demand. In presence of the random uncertainties, location planning problem is naturally a two-stage stochastic mixed integer nonlinear programming problem, which is hard to solve efficiently. Instead of using traditional sampling methods or robust optimization methods, we propose a novel analytical approach in this paper to solve the problem efficiently and optimally. In particular, analytical expressions are derived for efficiently evaluating the performance of a candidate RDG placement decision. In this way, the stochastic mixed integer nonlinear programming problem is equivalently transformed into a deterministic integer problem, which can be solved efficiently using off-the-shelf tools. Numerical results show that the optimal RDG placement can save up to $4.2\%$ of the long-term average cost and $80.59\%$ of the line losses on the IEEE 13-bus test feeder. In addition, our proposed approach effectively reduces the computational time by $99.51\%$ on the IEEE 123 node test feeder compared with other traditional sampling-based metaheuristic approaches. [ABSTRACT FROM PUBLISHER]

Demand response (DR) is envisioned as a promising solution to address the demand–supply mismatch issue in electric power systems. A new demand bidding model is considered in this paper to facilitate demand response trading in load curtailment form between the aggregator (buyer) and the large contract capacity customers (seller). Different types of customers with different load curtailment characteristics are considered in the demand bidding, leading to the formulation of a nonlinear optimization problem that is computationally expensive to solve. Delicate mechanisms are incorporated into the proposed purchase optimization scheme to reformulate the nonlinear programming problem into an equivalent linear model that can be solved efficiently. Simulation studies show that the proposed work is promising to minimize the total bidding purchase cost while satisfying the given load curtailment constraints that include the load curtailment and the load curtailment patterns of different customers. [ABSTRACT FROM PUBLISHER]

The problem to find the minimum number of controls to relieve overloads with ac power flow constraints (say, according to a state estimation) is formulated as mixed integer nonlinear programming (MINLP). To avoid the prohibitive computational time required by the classical MINLP approaches, we propose a three-stage method, i.e., screening, ranking, and detailed analysis, to tackle this problem. To make the proposed method applicable in online mode, a feasibility pump-based scheme is incorporated into the three-stage to solve the problem. It is shown that the proposed method can achieve computational tractability for large-scale systems. Numerical studies on the 300-bus system and the 2869-bus system reveal that the proposed method is about 1000 times faster than the branch and bound method, and the required controls for both methods are comparable. Simulation studies indicate the proposed method can effectively alleviate power system overloads in the online mode. [ABSTRACT FROM PUBLISHER]

For large power systems with huge proportions of industrial loads, dynamic var sources (DVSs) such as STATCOMs and generators provide significant protection against short-term voltage instability. Simultaneously optimizing the locations and capacities of STATCOMs and the settings for adjustment coefficients of generator excitation systems is a goal of this paper; thus, we formulate a multi-objective, mixed-integer dynamic optimization (MOMIDO) model subject to short-term voltage security and rotor-angle stability under multiple contingencies. We also present a reduced convex relaxation (RCR) method to handle STATCOM locations (which are integer variables) and to avoid their combinatorial explosion, and convert the MOMIDO model into a multi-objective nonlinear programming problem via Radau collocation. To obtain complete Pareto optimal solutions for the MOMIDO model, we propose a reduced wide normalized normal constraint (RWNNC) method by enlarging the Utopia plane so that the Pareto optimal solutions are covered completely by its vertical projection, while pruning redundant sections on the Utopia plane to reduce the amount of calculations. Moreover, the simulation results on an IEEE standard 39-bus system and a real provincial power system demonstrate that the proposed method can handle integer variables effectively and obtain complete Pareto optimal solutions. [ABSTRACT FROM PUBLISHER]

This paper proposes an automatic voltage control (AVC) system for power systems with limited continuous voltage control capability. The objective is to minimize the operational cost over a period, which consists of the power loss in the grid, the shunt switching cost, the transformer tap change cost, and the generator reactive power output cost. The problem is formulated in a multi-stage optimal reactive power flow (MORPF) framework, solved by the nonlinear programming techniques via a rolling process. The voltage uncertainty caused by wind power forecasting errors is considered in the optimal voltage/var dispatch by including a security margin in the voltage magnitude constraints. The robustness of the MORPF solution considering the voltage security margin is therefore improved. A platform is built for the offline investigations, employing the measurement data from the Danish national electricity control center, where study cases based on the western Danish power system demonstrate the superiority of the proposed AVC system in term of the cost minimization. Monte Carlo simulations are carried out to verify the proposed method on the robustness improvements. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive mathematical model to solve the restoration problem in balanced radial distribution systems. The restoration problem, originally modeled as mixed integer nonlinear programming, is transformed into a mixed integer second-order cone programming problem, which can be solved efficiently using several commercial solvers based on the efficient optimization technique family branch and bound. The proposed mathematical model considers several objectives in a single objective function, using parameters to preserve the hierarchy of the different objectives: 1) maximizing the satisfaction of the demand, 2) minimizing the number of switch operations, 3) prioritizing the automatic switch operation rather than a manual one, and 4) prioritizing especial loads. General and specialized tests were carried out on a 53-node test system, and the results were compared with other previously proposed algorithms. Results show that the mathematical model is robust, efficient, flexible, and presents excellent performance in finding optimal solutions. [ABSTRACT FROM PUBLISHER]

This paper presents a new method for solving the short-term unit commitment and loading problem for a specific hydropower system. Dynamic programming is used to compute maximum power output generated by a power plant. This information is then used as input of a two-phase optimization process. The first phase solves the relaxation of a nonlinear mixed-integer program in order to obtain the water discharge, reservoir volume and number of units working at each period in the planning horizon. The second stage solves a linear integer problem to determine which combination of turbines to use at each period. The goal is to maximize total energy produced over all periods of the planning horizon which consists of a week divided in hourly periods. Start-up of turbines are penalized. Numerical experiments are conducted on thirty different test cases for two Rio Tinto Alcan power plants with five turbines each. [ABSTRACT FROM AUTHOR]

Demand response (DR) refers to the consumers' activities for changing the load profile with the purpose of lowering cost, improving power quality or reliability of power system. Enhancement in participation of the DR is widely recognized as a profit-making pattern in distribution systems for both residential units (to increase their benefits) and distribution companies (DISCO) (to reduce their peak demand and costs). The target of this research is concentrated on proposing a new strategy for optimal scheduling of flexible loads for the next day. Then, the day ahead pricing (DAP) is modeled using the inclining block rates (IBR), assumed for retail electricity markets, to investigate the efficiency of the proposed strategy. At the same time, the appliances stochastic time of use (ASTOU) are taken into account in residential units for non-controllable part of the load during a day stochastically. Among five various copulas, the Gaussian copula (GC) function shows the best performance in modeling and estimation of non-controllable load consumption. Finally, simulations, performed with the GAMS, illustrate the effectiveness of the suggested approach which is formulated as a stochastic nonlinear programming (NLP) modeled by the GC. Notice that copulas use samples of real data gathered from residential units. [ABSTRACT FROM PUBLISHER]

This paper proposes a vehicle-to-grid reactive power support (V2GQ) strategy for optimal coordinated voltage regulation in distribution networks with high distributed generation (DG) penetration. The proposed algorithm employs plug-in electric vehicles (PEVs), DG, and on-load tap changer (OLTC) to satisfy PEV charging demand and grid voltage requirements with relaxed tap operation, and minimum DG active power curtailment. The voltage regulation problem is formulated as a nonlinear programming and consists of three consecutive stages, in which successive stages apply the outputs of their preceding stages as constraints. The first stage aims to maximize the energy delivered to PEVs to assure PEV owner satisfactions. The second stage maximizes the DG extracted active power. Third stage minimizes the voltage deviation from its nominal value utilizing the available PEV and DG reactive powers. The main implicit objective of the third stage problem is relaxing the OLTC tap operation. In addition, the conventional OLTC control is replaced by a proposed centralized controller that utilizes the output of the third stage to set its tap position. Real-time simulations are developed to demonstrate the effectiveness of the proposed optimal coordinated algorithm on a typical distribution network using OPAL-RT real-time simulator (RTS) in a hardware-in-the-loop (HIL) application. [ABSTRACT FROM PUBLISHER]

This paper proposes a bi-level branch-and-bound ((B&B) method to solve the economic dispatch problem with prohibited zones and network losses. The approach employs binary variables for each prohibited zone and utilizes the B-coefficient for network losses, which can be transformed into a mixed-integer quadratically constrained quadratic programming (MIQCQP), where linear relaxation technique is applied on each bilinear term. Due to the complexity in solving the MIQCQP problem, this paper proposes a bi-level B&B method to achieve global optimum. A spatial B&B method is utilized in the higher level to solve the quadratically constrained quadratic programming (QCQP) problem, whereas a simple B&B method is employed in the lower level to solve a mixed-integer quadratic programming (MIQP) problem. The bi-level B&B algorithm that combines spatial and simple B&B methods is actually a deterministic optimization method and can produce global optimal solutions. Numerical results on 6-unit, 15-unit, and 40-unit test systems show that the bi-level B&B method can solve the MIQCQP problem with superior solution quality and convergence characteristics. [ABSTRACT FROM PUBLISHER]

This paper investigates joint scheduling problem of large-scale smart appliances and batteries (e.g., in a smart building), to minimize electricity payment, user's dissatisfaction and battery loss under kinds of constraints. Due to the binary nature of charge and discharge states of battery, this problem is formulated as a constrained mixed-integer nonlinear program. In order to solve it efficiently, a distributed mixed optimization approach is proposed. First, Lagrangian relaxation is applied to decompose the original problem into two sets of subproblems, each of which corresponds to scheduling on appliance/battery. Then, the battery scheduling subproblem is formulated as a mixed-integer linear program and tackled by Benders decomposition. The main advantages of the proposed approach are the distributed implementation and low computational complexity, as shown by simulations. [ABSTRACT FROM PUBLISHER]

This paper presents a novel profit-based model for multistage distribution network expansion planning. In the proposed model, some capabilities of the distribution automation system are considered to achieve a plan that is more compatible with the strategic plan towards a smart distribution grid. Moreover, a method for reliability evaluation of a distribution network in the planning studies is proposed. The objective function of the planning problem is the net present value of the company's profit. The problem is solved by employing the genetic algorithm approach as a promising technique in solving the mixed-integer nonlinear programming problem. The proposed method is evaluated on an illustrative test network and the obtained results are presented and discussed. [ABSTRACT FROM PUBLISHER]

In this paper a mixed integer quadratic programming (MIQP) is proposed to solve the dynamic economic dispatch (DED) with valve-point effect (VPE) where the non-linear and non-smooth cost caused by VPE is piecewise linearized. However if the DED with VPE is directly solved by the MIQP in a single step, the optimization suffers convergence stagnancy and will run out of memory. In this paper the multi-step method, the warm start technique and the range restriction scheme are combined with the MIQP. The optimization process can then break the convergence stagnancy and the computation efficiency can be greatly improved. When the system loss is considered, the loss formula is piecewise linearized. A post-processing procedure is proposed to eliminate the approximation error caused by linearization of the loss formula. The effectiveness of the proposed method is demonstrated by seven cases and the results are compared with those obtained by the previous published methods. [ABSTRACT FROM PUBLISHER]

Rapidly increasing the penetration level of renewable energies has imposed new challenges to the operation of power systems. Inability or inadequacy of these resources in providing inertial and primary frequency responses is one of the important challenges. In this paper, this issue is addressed within the framework of security-constrained unit commitment (SCUC) by adding new constraints representing the system frequency response. A modified system frequency response model is first derived and used to find analytical representation of system minimum frequency in thermal-dominant multi-machine systems. Then, an effective piecewise linearization (PWL) technique is employed to linearize the nonlinear function representing the minimum system frequency, facilitating its integration in the SCUC problem. The problem is formulated as a mixed-integer linear programming (MILP) problem which is solved efficiently by available commercial solvers. The results indicate that the proposed method can be utilized to integrate renewable resources into power systems without violating system frequency limits. [ABSTRACT FROM PUBLISHER]

In this paper, a nonlinear model for medium-term hydro-thermal scheduling problems with transmission constraints is presented. A nonlinear formulation of hydro power production functions of discharge and storage is used for proper representation of head variation in cascaded reservoirs. Transmission networks are formulated as a direct-current power flow model with support to load attainment over multiple levels representing peak and off-peak hours, enabling varying degrees of both network topology and load balance nodes representation over time stages. Sparse matrix structures that arise from the mathematical formulation allow fast decoupled computation of search directions in a primal-dual interior-point framework. Computational tests with very large case studies are presented. Analysis of numerical algorithm convergence is provided along with insightful optimal solution interpretative commentary. [ABSTRACT FROM PUBLISHER]

This paper describes a new probabilistic model for generation and transmission expansion planning (G&TEP) problem considering reliability criteria. Probabilistic reliability criteria accounts for random generator or line outages with known historical forced outage rates (FOR). The resultant model considers the installation and operation costs as well as the cost of expected energy not supplied (EENS) to optimally determine the number and location of new generating units and circuits in the network, power generation capacity for those units and the voltage phase angle at each node. Also, efficient linear formulations are introduced in this paper to deal with the nonlinear nature of the problem including objective functions and constraints. Modified 6-bus test system, IEEE 24-bus RTS and IEEE 118-bus test system are utilized to illustrate the effectiveness of the proposed framework. [ABSTRACT FROM PUBLISHER]

This paper applies recently developed mixed-integer programming (MIP) tools to the problem of optimal siting and sizing of distributed generators in a distribution network. We investigate the merits of three MIP approaches for finding good installation plans: a full AC power flow approach, a linear DC power flow approximation, and a nonlinear DC power flow approximation with quadratic loss terms, each augmented with integer generator placement variables. A genetic algorithm-based approach serves as a baseline for the comparison. A simple knapsack problem method involving generator selection is presented for determining lower bounds on the optimal design objective. Solution methods are outlined, and computational results show that the MIP methods, while lacking the speed of the genetic algorithm, can find improved solutions within conservative time requirements and provide useful information on optimality. [ABSTRACT FROM AUTHOR]

This paper considers optimal transmission switching (OTS) to reduce generation cost by removing lines from service. A mixed integer program (MIP) has been proposed to solve the OTS problem, based on the linear direct current optimal power flow (DCOPF) model. Because of excessive computation times for large, real systems, the MIP model has been followed by some heuristics, also based on the DCOPF, to obtain near-optimal solutions quickly. However, the approximations in the DCOPF model may lead to poor choices of lines to remove from service. We assess the quality of line removal recommendations that rely on a previously published, DCOPF-based heuristic, by estimating actual cost reduction with the exact ACOPF model, using the IEEE 118-bus and 300-bus test systems with several demand levels. We also extend this heuristic to be based on the ACOPF and compare the quality of its recommendations to those of the DCOPF-based heuristic. The DCOPF-based heuristic performs very poorly in several cases, even leading to cost increases sometimes. There is a need for approximations to the ACOPF which are accurate enough to produce reliably good results for OTS heuristics, but fast enough for practical use. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-year multi-objective planning algorithm for enabling distribution networks to accommodate high penetrations of plug-in electric vehicles (PEVs) in conjunction with renewable distributed generation (DG). The proposed algorithm includes consideration of uncertainties and will help local distribution companies (LDC) better assess the expected impacts of PEVs on their networks and on proposed renewable DG connections. The goal of the proposed algorithm is to minimize greenhouse gas emissions and system costs during the planning horizon. An approach based on a non-dominated sorting genetic algorithm (NDSGA) is utilized to solve the planning problem of determining the optimal level of PEV penetration as well as the location, size, and year of installation of renewable DG units. The planning problem is defined in terms of multi-objective mixed integer nonlinear programming. The outcomes of the planning problem represent the Pareto frontier, which describes the optimal system solutions, from which the LDC can choose the system operating point, based on its preferences. [ABSTRACT FROM PUBLISHER]