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New generation, storage, demand management, and smart grid technologies have the potential to make the grid of 2050 very different from today's power system. Can market designers keep up, and what do they need to pay attention to now? Before offering a few answers to these questions, we first give some historical perspective on the creation of markets for electric power and how those markets have been adapted to new needs. [ABSTRACT FROM AUTHOR]

In this paper, we model topology control through transmission switching as a recourse action in the day-ahead operation of power systems with large-scale renewable generation resources. We prove that transmission switching could only reduce the linear objective value of direct current optimal power flow when congestion exists. However, we also show, through a simple example, that unit commitment cost could be reduced by transmission switching even in the absence of congestion. To solve the stochastic unit commitment with topology control within reasonable computational time, we proposed a heuristic that first decomposes a practical system into zones and then solves the problem for each zone in parallel. The benefit of topology control recourse is demonstrated on a network representing the Central European System. We compare the costs of the network with different loading and renewable generation conditions. The cost reduction of the test system can reach 3.34% with heavy load and large-scale renewable generation while in a single zone the cost reduction can be above 7%. [ABSTRACT FROM PUBLISHER]

This paper presents a distribution locational marginal pricing (DLMP) method through chance constrained mixed-integer programming (MIP) designed to alleviate the possible congestion in the future distribution network with high penetration of electric vehicles (EVs). In order to represent the stochastic characteristics of the EV driving patterns, a chance constrained optimization of the EV charging is proposed and formulated through MIP. With the chance constraints in the optimization formulations, it guarantees that the failure probability of the EV charging plan fulfilling the driving requirement is below the predetermined confidence parameter. The efficacy of the proposed approach was demonstrated by case studies using a 33-bus distribution system of the Bornholm power system and the Danish driving data. The case study results show that the DLMP method through chance constrained MIP can successfully alleviate the congestion in the distribution network due to the EV charging while keeping the failure probability of EV charging not meeting driving needs below the predefined confidence. [ABSTRACT FROM PUBLISHER]

The ACOPF is at the core of competitive electricity market design. It is the only ac-based algorithm that simultaneously co-optimizes real and reactive power dispatch for steady-state operations on ac power systems. In practice, independent system operators (ISOs) oversimplify the physical problem and settle the markets on locational marginal price (LMPs) based on real power but do not price reactive power dispatch or voltage control. This work proposes a market dispatch and pricing procedure based on the ACOPF to provide a more complete pricing mechanism. We formulate the dual problem of the SLP-IV-ACOPF as shown in [1], which is a successive linear program shown to solve the ACOPF to an acceptable quality of convergence to a best-known solution with linear scaling of computational time in proportion to network size. Therefore, the dual problem is also a linear program; as a result, the marginal value pricing of the optimal solution to the dual problem supports the market dispatch due to strong duality, i.e., this solution technique results in a revenue adequate and more complete market design. Furthermore, we show how to distribute the complete real-time market settlements. The analysis includes a direct comparison to DCOPF-based approaches similar to those applied in current ISO markets. [ABSTRACT FROM AUTHOR]

Improved formulations of and solution techniques for the alternating current optimal power flow (ACOPF) problem are critical to improving current market practices in economic dispatch. We introduce the IV-ACOPF formulation that unlike canonical ACOPF formulations–which represent network balancing through nonlinear coupling–is based on a current injections approach that linearly couple the quadratic constraints at each bus; yet, the IV-ACOPF is mathematically equivalent to the canonical ACOPF formulation. We propose a successive linear programming (SLP) approach to solve the IV-ACOPF, which we refer to as the SLP IV-ACOPF algorithm. The SLP IV-ACOPF leverages commercial LP solvers and can be readily extended and integrated into more complex decision processes, e.g., unit commitment and transmission switching. We demonstrate with the standard MATPOWER test suite an acceptable quality of convergence to a best-known solution and linear scaling of computational time in proportion to network size. [ABSTRACT FROM PUBLISHER]

This paper presents the distribution locational marginal pricing (DLMP) method through quadratic programming (QP) designed to alleviate the congestion that might occur in a distribution network with high penetration of flexible demands. In the DLMP method, the distribution system operator (DSO) calculates dynamic tariffs and publishes them to the aggregators, who make the optimal energy plans for the flexible demands. The DLMP through QP instead of linear programing as studied in previous literatures solves the multiple solution issue of the aggregator optimization which may cause the decentralized congestion management by DLMP to fail. It is proven in this paper, using convex optimization theory, the aggregator's optimization problem through QP is strictly convex and has a unique solution. The Karush-Kuhn-Tucker (KKT) conditions and the unique solution of the aggregator optimization ensure that the centralized DSO optimization and the decentralized aggregator optimization converge. Case studies using a distribution network with high penetration of electric vehicles (EVs) and heat pumps (HPs) validate the equivalence of the two optimization setups, and the efficacy of the proposed DLMP through QP for congestion management. [ABSTRACT FROM PUBLISHER]

We present a parallel implementation of Lagrangian relaxation for solving stochastic unit commitment subject to uncertainty in renewable power supply and generator and transmission line failures. We describe a scenario selection algorithm inspired by importance sampling in order to formulate the stochastic unit commitment problem and validate its performance by comparing it to a stochastic formulation with a very large number of scenarios, that we are able to solve through parallelization. We examine the impact of narrowing the duality gap on the performance of stochastic unit commitment and compare it to the impact of increasing the number of scenarios in the model. We report results on the running time of the model and discuss the applicability of the method in an operational setting. [ABSTRACT FROM PUBLISHER]

We present a stochastic unit commitment model for assessing the impacts of the large-scale integration of renewable energy sources and deferrable demand in power systems in terms of reserve requirements. We analyze three demand response paradigms for assessing the benefits of demand flexibility: the centralized co-optimization of generation and demand by the system operator, demand bids and the coupling of renewable resources with deferrable loads. We motivate coupling as an alternative for overcoming the drawbacks of the two alternative demand response options and we present a dynamic programming algorithm for coordinating deferrable demand with renewable supply. We present simulation results for a model of the Western Electricity Coordinating Council. [ABSTRACT FROM PUBLISHER]

This paper presents an integrated distribution locational marginal pricing (DLMP) method designed to alleviate congestion induced by electric vehicle (EV) loads in future power systems. In the proposed approach, the distribution system operator (DSO) determines distribution locational marginal prices (DLMPs) by solving the social welfare optimization of the electric distribution system which considers EV aggregators as price takers in the local DSO market and demand price elasticity. Nonlinear optimization has been used to solve the social welfare optimization problem in order to obtain the DLMPs. The efficacy of the proposed approach was demonstrated by using the bus 4 distribution system of the Roy Billinton Test System (RBTS) and Danish driving data. The case study results show that the integrated DLMP methodology can successfully alleviate the congestion caused by EV loads. It is also shown that the socially optimal charging schedule can be implemented through a decentralized mechanism where loads respond autonomously to the posted DLMPs by maximizing their individual net surplus. [ABSTRACT FROM PUBLISHER]

We present a two-stage stochastic programming model for committing reserves in systems with large amounts of wind power. We describe wind power generation in terms of a representative set of appropriately weighted scenarios, and we present a dual decomposition algorithm for solving the resulting stochastic program. We test our scenario generation methodology on a model of California consisting of 122 generators, and we show that the stochastic programming unit commitment policy outperforms common reserve rules. [ABSTRACT FROM PUBLISHER]

There is currently a national push to create a smarter grid. Currently, the full control of transmission assets is not built in network optimization models. With more sophisticated modeling of transmission assets, it is possible to better utilize the current infrastructure to improve the social welfare. Co-optimizing the generation with the network topology has been shown to reduce the total dispatch cost. In this paper, we propose the concept of just-in-time transmission. This concept is predicated on the fact that transmission that is a detriment to network efficiency can be kept offline when not needed and, with the proper smart grid/advanced technology, can be switched back into service once there is a disturbance. We determine which lines to have offline based on the optimal transmission switching model previously proposed. A secondary topic of this paper focuses on flowgate bidding. Approved by the Federal Energy Regulatory Commission and implemented within the SPP and NYISO networks, flowgate bidding is defined as allowing a line's flow to exceed its rated capacity for a short period of time for a set penalty, i.e., price. We demonstrate the effectiveness of these models by testing them on large-scale ISO network models. [ABSTRACT FROM AUTHOR]

Currently, there is a national push for a smarter electric grid, one that is more controllable and flexible. The full control of transmission assets are not currently built into electric network optimization models. Optimal transmission switching is a straightforward way to leverage grid controllability: to make better use of the existing system and meet growing demand with existing infrastructure. Previous papers have shown that optimizing the network topology improves the dispatch of electrical networks. Such optimal topology dispatch can be categorized as a smart grid application where there is a co-optimization of both generators and transmission topology. In this paper we present a co-optimization formulation of the generation unit commitment and transmission switching problem while ensuring N-1 reliability. We show that the optimal topology of the network can vary from hour to hour. We also show that optimizing the topology can change the optimal unit commitment schedule. This problem is large and computationally complex even for medium sized systems. We present decomposition and computational approaches to solving this problem. Results are presented for the IEEE RTS 96 test case. [ABSTRACT FROM AUTHOR]

In this paper, we continue to analyze optimal dispatch of generation and transmission topology to meet load as a mixed integer program (MIP) with binary variables representing the state of the transmission element (line or transformer). Previous research showed a 25% savings by dispatching the IEEE 118-bus test case. This paper is an extension of that work. It presents how changing the topology affects nodal prices, load payment, generation revenues, cost, and rents, congestion rents, and flowgate prices. Results indicate that changing the topology to cut costs typically results in lower load payments and higher generation rents for this network. Computational issues are also discussed. [ABSTRACT FROM AUTHOR]

Many wholesale electricity markets call on the independent system operator (ISO) to determine day-ahead schedules for generators based on a centralized unit commitment. Up until recently, the Lagrangian relaxation (LR) algorithm was the only practical means of solving an ISO-scale unit commitment problem, and it was the solution technique used by most ISOs. Johnson et al. [1] demonstrate, however, that equity, incentive, and efficiency issues will arise from use of LR solutions, because different commitments that are similar in terms of total system costs can result in different surpluses to individual units. Recent advances in computing capabilities and optimization algorithms now make solution of the mixed-integer programming (MIP) formulation by means of branch and bound (B&B) tractable, often with optimality gaps smaller than those of LR algorithms, which has led some ISOs to adopt B&B algorithms and others proposing to do so. With the move towards B&B, one obvious question is whether the use of MIP will eliminate or reduce the issues with LR raised by Johnson et a!. Using actual market data from an ISO, we demonstrate that both LR and MW solutions will suffer the same equity issues, unless the ISO unit commitment problems can be solved to complete optimality within the allotted timeframe—which is beyond current computational capabilities. Our results further demonstrate that the size of the payoff deviations are not mono- tone in the size of the optimality gap, meaning smaller optimality gaps from B&B will not necessarily mitigate the issues Johnson et al. raise. We show that the use of "make-whole" payments, which ensure units recover any startup and no-load costs not recovered by inframarginal energy rents, can help to reduce surplus volatility and differences to some extent. [ABSTRACT FROM AUTHOR]

From an economic perspective, a common criterion for assessing the merits of a transmission investment is its impacts on social welfare. The underlying assumption in using this criterion is that side payments may be used to distribute the social gains among all market players. In reality, however, since the impacts of an electricity transmission project on different players may vary, such side payments are rather difficult to implement. This paper focuses on different economic criteria that should be considered when planning electricity transmission investments. We propose an electricity transmission investment assessment methodology that is capable of evaluating the economic impacts on the various effected stakeholders and account for strategic responses that could enhance or impede the investment's objectives. We formulate transmission planning as an optimization problem under alternative conflicting objectives and investigate the policy implications of divergent expansion plans resulting from the planner's level of anticipation of strategic responses. We find that optimal transmission expansion plans may be very sensitive to supply and demand parameters. We also show that the transmission investments have significant distributional impact, creating acute conflicts of interests among market participants. We use a 32-bus representation of the main Chilean grid to illustrate our results. [ABSTRACT FROM AUTHOR]

We examine the impact of wholesale markets on operations of the bulk power system and elaborate some basic implications of engineering practices for designs of wholesale markets. This analysis is intended to provide a basis for enhancements to existing principles of engineering management. Wholesale markets bring economic and financial aspects that alter the context in which system operations are conducted, and introduce incentive and benefit--cost considerations that might alter operating procedures that previously were based on reliability considerations. The principles addressed are those relevant to the interface between engineering aspects of system operations, and economic aspects of market operations. We outline ways that engineering practices developed in the era of vertically integrated utilities might be adapted to the wholesale markets introduced since restructuring began in 1998 in the United States [ABSTRACT FROM AUTHOR]

Presents a three-node example under the assumption that the proposed market based transmission load relief (TLR) process finds the correct prices for transmission of electric power systems. Main contenders for market based TLR approach; Regional decomposition through partial dualization; Details of the alternating current optimal power flow model; Discussion of price adjustment.

A response from the authors of the article "Distribution Locational Marginal Pricing for Optimal Electric Vehicle Charging Management" in the January 2014 issue is presented.