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This paper deals with the problem of transmission cost allocation (TCA) in very large networks with multiple interconnected regions or countries. The basis of one scheme recently put forward is a single-area TCA algorithm from which inter-regional compensations are computed. One concern about this approach is that an international operator (IO) must have access to detailed information that autonomous regions or countries may not be willing or able to share. This led to the development of a new multiarea TCA scheme, called multiarea decoupled (MAD), in which each region carries out its own TCA, while the IO carries out a region-wise TCA on the network of tie lines. In MAD, the IO does not require detailed and possibly proprietary information about regional networks, relying only on the characteristics of the tie-line network and the extent to which each region uses such a network. This is a key issue, especially in North America, considering FERC's intention to provide Regional Transmission Organizations (RTOs) with exclusive autonomy to provide transmission service and to set and administer their own transmission-use tariffs. Numerical studies on the proposed scheme are described, including a four-area system based on the IEEE reliability test system (RTS) network. Index Terms--Equivalent bilateral exchange, flow based, multiarea networks, proportional sharing, tie lines, transmission cost allocation (TCA).

This letter describes a new technique for state estimation observability analysis. This technique is computationally efficient and based on calculating the null space of the measurement Jacobian matrix. The technique is illustrated through a clarifying example. Conclusions are duly drawn. Index Terms--Null space, observability, state estimation.

Increasing use of natural gas for electricity production places added strains on pipeline systems that are used for transporting fuel. Pipeline constraints require power system operators to account for natural gas-supply restrictions in their operational processes. This paper proposes separate optimization models for clearing day-ahead wholesale markets for scheduling power and natural gas systems. We then develop a market-based mechanism that allows for efficient co-ordination of the two systems. Importantly, the co-ordination mechanism only requires the exchange of fuel-price, -supply, and -demand information between the two markets. This can be contrasted with other co-ordination mechanisms that require operations of the two systems by a single entity. Thus, we provide a computationally tractable co-ordination mechanism that does not require the exchange of proprietary information between natural gas and electricity system operators. We demonstrate the effectiveness and scalability of the technique using a numerical example. [ABSTRACT FROM AUTHOR]

The recent leak of the Aliso Canyon natural gas storage facility calls for coordinated planning of natural gas and electric power systems with specific consideration of electrical energy storage. This paper proposes a coordinated planning model that fills this need. The model is formulated as a two-stage stochastic optimization problem, in which electricity- and natural gas-demand growth and natural gas-supply uncertainties are represented. We analyze the tradeoff between building electrical, natural gas, and electrical energy storage infrastructure. The sensitivity of electrical energy storage investment to the modeling of the operating conditions is also studied. The model is tested using a California-based case study. [ABSTRACT FROM AUTHOR]

This paper focuses on the estimation of the rotor speeds of synchronous machines by means of phasor measurement units. This estimation is aimed at online monitoring of electromechanical transients and transient stability analysis. The proposed technique is based on the concept of frequency divider formula recently published by the first two authors in these Transactions. The dynamic state estimation is formally stated as a convex optimization problem and a thorough discussion of the sensitivity analysis of the optimal solution is provided. The case study considers a dynamic 1479-bus model of the all-island Irish system and discusses the effect of bad data, noise, and latency on the proposed estimation technique. [ABSTRACT FROM AUTHOR]

Many principles underlying the design of restructured electricity markets that are in-use today were developed over three decades ago when power systems were considerably different than today’s and tomorrow’s systems are. Systems of the past typically relied on large dispatchable thermal generators to supply energy. This can be contrasted with power systems today, which are experiencing rising penetrations of weather-dependent renewable energy sources that have limited dispatchability. Moreover, many power systems are experiencing growing adoption of distributed energy resources and novel uses of electric energy by end customers, which adds to demand uncertainty and variability. However, these technologies also provide opportunities for more active participation of the demand-side in maintaining system reliability and service quality. Given these marked changes in the architecture of electric power systems, we are at a unique point at which the tenets of restructured electricity market design can be re-evaluated. While this re-examination is largely driven by changes in power system designs, we can also rely on lessons learned from the past three decades of market-restructuring experience. In this paper, we highlight some of the challenges in designing electricity markets brought about by changes in system designs. We also discuss a number of lessons learned from market designs that have been implemented. We then suggest some important principles that should underlie future reforms of electricity market designs and raise design questions that require further research and examination. [ABSTRACT FROM AUTHOR]

The interdependence between natural gas and electric power systems is becoming increasingly tight, as the share of natural gas-fired units increases. Within this context, this paper addresses the coordinated expansion planning of natural gas and power systems. We analyze the tradeoff of building natural gas-related facilities (e.g., natural gas pipelines and natural gas-fired generation units) versus electric-power-related facilities (e.g., transmission lines and other generation units). We use a two-stage stochastic optimization model that provides an appropriate balance between accuracy and computational tractability and represents uncertainty pertaining to electricity and natural gas demands. We show the importance of representing uncertainty by computing the value of stochastic solution, which is significant. We analyze the functioning of the model through a small example and a case study based on the IEEE 118-bus system. Computational results confirm the need for detailed representation of both the natural gas and power systems to achieve investment decisions that are well coordinated and optimal. [ABSTRACT FROM PUBLISHER]

Power system capacity-expansion models are typically intractable if every operating period is represented. This issue is normally overcome by using a subset of representative operating periods. For instance, representative operating hours can be selected by discretizing the load-duration curve, which captures the effect of load levels on system-operation costs. This approach is inappropriate if system-operating costs depend on parameters other than load (e.g., renewable-resource availability) or if there are important intertemporal operating constraints (e.g., generator-ramping limits). This paper proposes the use of representative operating days, which are selected using clustering, to surmount these issues. We propose two hierarchical clustering techniques, which are designed to capture the important statistical features of the parameters (e.g., load and renewable-resource availability), in selecting representative days. This includes temporal autocorrelations and correlations between different locations. A case study, which is based on the Texan power system, is used to demonstrate the techniques. We show that our proposed clustering techniques result in investment decisions that closely match those made using the full unclustered dataset. [ABSTRACT FROM PUBLISHER]

This paper proposes an efficient solution approach based on Benders’ decomposition to solve a network-constrained ac unit commitment problem under uncertainty. The wind power production is the only source of uncertainty considered in this paper, which is modeled through a suitable set of scenarios. The proposed model is formulated as a two-stage stochastic programming problem, whose first-stage refers to the day-ahead market, and whose second-stage represents real-time operation. The proposed Benders’ approach allows decomposing the original problem, which is mixed-integer nonlinear and generally intractable, into a mixed-integer linear master problem and a set of nonlinear, but continuous subproblems, one per scenario. In addition, to temporally decompose the proposed ac unit commitment problem, a heuristic technique is used to relax the inter-temporal ramping constraints of the generating units. Numerical results from a case study based on the IEEE one-area reliability test system (RTS) demonstrate the usefulness of the proposed approach. [ABSTRACT FROM AUTHOR]

Renewable energy sources are here to stay for a number of important reasons, including global warming and the depletion of fossil fuels. We explore in this paper how a thermal-dominated electric energy system can be transformed into a renewable-dominated one. This study relies on a stochastic programming model that allows representing the uncertain parameters plaguing such long-term planning exercise. Being the final year of our analysis 2050, we represent the transition from today to 2050 by allowing investment in both production and transmission facilities, with the target of achieving a renewable-dominated minimum-cost system. The methodology developed is illustrated using a realistic large-scale case study. Finally, policy conclusions are drawn. [ABSTRACT FROM PUBLISHER]

This paper considers the yearly maintenance scheduling of generating units within a market environment. Each producer schedules its units' maintenance periods to maximize its revenue using a bilevel approach. The upper-level problem of this bilevel model seeks maximum revenue and contains unit scheduling constraints, while the lower-level problems represent the market clearing process under different operating conditions. This single producer maintenance problem can be recast as a mathematical program with equilibrium constraints (MPEC). Since the MPECs of all producers have to be considered simultaneously and the market clearing process is common to all of them, the proposed formulation for maintenance scheduling is an equilibrium problem with equilibrium constraints (EPEC) corresponding to a multiple-leader-common-follower game. The solution of this EPEC is a set of equilibria, in which none of the producers is able to increase its revenue unilaterally by changing the maintenance periods of its generating units. [ABSTRACT FROM PUBLISHER]

This paper provides a methodology to restore transient stability. It relies on a well-behaved optimal power flow model with embedded transient stability constraints. The proposed methodology can be used for both dispatching and redispatching. In addition to power flow constraints and limits, the resulting optimal power flow model includes discrete time equations describing the time evolution of all machines in the system. Transient stability constraints are formulated by reducing the initial multi-machine model to a one-machine infinite-bus equivalent. This equivalent allows imposing angle bounds that ensure transient stability. The proposed optimal power flow model is tested and analyzed using an illustrative nine-bus system, the well-known New England 39-bus system, a ten-machine system, and a real-world 1228-bus system with 292 synchronous machines. [ABSTRACT FROM AUTHOR]

State estimation measurements within a substation are routinely considered Gaussian and independent. In this paper, the questionable independence assumption is dropped and a statistical procedure is proposed to estimate the measurement variance-covariance matrix. The well-known weighted least squares technique for estimation is then modified to take into account measurement dependencies. Two case studies are analyzed and conclusions duly drawn. [ABSTRACT FROM AUTHOR]

We present a bilevel model for transmission expansion planning within a market environment, where producers and consumers trade freely electric energy through a pool. The target of the transmission planner, modeled through the upper-level problem, is to minimize network investment cost while facilitating energy trading. This upper-level problem is constrained by a collection of lower-level market clearing problems representing pool trading, and whose individual objective functions correspond to social welfare. Using the duality theory the proposed bilevel model is recast as a mixed-integer linear programming problem, which is solvable using branch-and-cut solvers. Detailed results from an illustrative example and a case study are presented and discussed. Finally, some relevant conclusions are drawn. [ABSTRACT FROM AUTHOR]

This paper presents a bilevel programming approach to solve the medium-term decision-making problem faced by a power retailer. A retailer decides its level of involvement in the futures market and in the pool as well as the selling price offered to its potential clients with the goal of maximizing the expected profit at a given risk level. Uncertainty on future pool prices, client demands, and rival-retailer prices is accounted for via stochastic programming. Unlike in previous approaches, client response to retail price and competition among rival retailers are both explicitly considered in the proposed bilevel model. The resulting nonlinear bilevel programming formulation is transformed into an equivalent single-level mixed-integer linear programming problem by replacing the lower-level optimization by its Karush-Kuhn-Tucker optimality conditions and converting a number of nonlinearities to linear equivalents using some well-known integer algebra results. A realistic case study is solved to illustrate the efficient performance of the proposed methodology. [ABSTRACT FROM AUTHOR]

A generating unit can sell its production in the pool facing the financial risk inherent to volatile pool prices. Alternatively, it can sell its production through the futures market at comparatively stable but smaller average prices. Unlike the pool, forward contracts involve an obligation to sell during a specified time period, and, if the unit fails, it should buy energy from the pool to meet its futures market commitments. Hence, taking into account its forced outage rate, the unit should determine the appropriate mix of pool and futures-market involvement so that its expected profit is maximized for a prespecified risk level on profit variability. Within this context, this paper analyzes the impact of the degree of unavailability of the generating unit on its forward contracting decisions. A detailed case study illustrates the analysis performed. [ABSTRACT FROM AUTHOR]

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

To procure the electric energy to be sold to its clients, a retailer has to face two major difficulties. While buying electric energy, it must cope with uncertain pool prices or sign forward contracts at higher average prices. While selling electricity, it should handle the uncertainty of the end-user demand and the fact that customers might choose a different retailer if the selling price is not competitive enough. We propose a risk-constrained stochastic programming framework to decide which forward contracts the retailer should sign and at which price it must sell electricity so that its expected profit is maximized at a given risk level. The developed model takes into account the elastic behavior of end users with respect to the selling price offered by the retailer. A realistic case study illustrates the methodology proposed. [ABSTRACT FROM AUTHOR]

Considering the viewpoint of a retailer, this paper analyzes the problem of setting up contracts on both the supplier and end-user sides to maximize profits while maintaining an acceptable level of settlement risk. The proposed stochastic optimization model can assist retailers with these efforts and guide them in their contractual arrangements. A realistic example illustrates the capabilities of the methodology proposed. [ABSTRACT FROM AUTHOR]

This paper considers a profit-maximizing thermal producer that participates in a sequence of spot markets, namely, day-ahead, automatic generation control (AG C), and balancing markets. The producer behaves as a price-taker in both the day-ahead market and the AGC market but as a potential price-maker in the volatile balancing market. The paper provides a stochastic programming methodology to determine the optimal bidding strategies for the day-ahead market. Uncertainty sources include prices for the day-ahead and AGC markets and balancing market linear price variations with the production of the thermal producer. Results from a realistic case study are reported and analyzed. Conclusions are duly drawn. [ABSTRACT FROM AUTHOR]

Within an optimal power flow market clearing framework, this paper provides expressions to compute the sensitivities of locational marginal prices with respect to power demands. Sensitivities with respect to other parameters can also be obtained. An example and a case study are used to illustrate the expressions derived. [ABSTRACT FROM AUTHOR]

This paper proposes a novel technique for representing system security constraints that properly include voltage stability limits in the operation of competitive electricity markets. The market-clearing algorithm is modeled as a voltage stability constrained optimal power flow (OPF) problem, while the distance to the closest critical power flow solution is represented by means of a loading parameter and evaluated using a continuation power flow (CPF) technique. Sensitivities obtained at the OPF step are used to estimate power directions for the CPF method, while the CPF analysis provides the loading parameter to be used in the OPF problem based on an N - 1 contingency criterion. The OPF and the CPF steps are repeated until the maximum loading parameter is found, thus providing optimal solutions considering both proper market conditions and security margins. Two benchmark systems with both supply and demand bidding are used to illustrate and test the proposed technique. [ABSTRACT FROM AUTHOR]

This paper analyzes the market-clearing formulation with stochastic security developed in its companion paper through two case studies solved using mixed-integer linear programming techniques. The generation and reserve schedules as well as the nodal prices of energy and security are assessed under various conditions such as a) line flow limits, b) when nonspinning reserve is excluded from the formulation, c) demand-side valuation of energy not served, d) generator ramping limits, and e) the set of pre-selected contingencies. [ABSTRACT FROM AUTHOR]

This paper addresses the self scheduling problem of a price-taker power producer. It focuses on risk modeling, emphasizing the tradeoff existing between maximum profit and minimum risk. The paper analyzes a self-scheduling model that considers simultaneously profit and risk. This model Is formulated as a mixed-Integer quadratic programming problem, which Is solved using commercially available software. Relevant results from a realistic case study are discussed. [ABSTRACT FROM AUTHOR]

This paper presents a novel methodology for allocating the cost of a transmission network to its users based on the principle of equivalent bilateral exchanges, which states that after all physical laws governing the flow of power have been met, each demand is assigned a fraction of each generation and each generator is assigned a fraction of each demand in a uniform manner. Transmission cost allocation based on this principle presents several advantages, namely, independence from the choice of the slack bus, recognition of counter-flows, and transmission use charges that are stable and always positive. [ABSTRACT FROM AUTHOR]

Presents a multiperiod electricity auction market tool that take into account transmission congestion and losses, along with several other factors. Formulation of the pool auction; Linearization of the network constraint; Application of the proposed approach to the Institute of Electrical and Electronic Engineers' Reliability Test System.

The article discusses various reports published within the issue, including one on the economic criteria that should be considered to efficiently carry out expansion planning for energy transmission and another on the planning issues raised by high temperature-low sag conductors.

Rising penetrations of variable renewable generation in electric power systems can raise operational challenges. One is that renewables can increase the need for dispatchable generation with fast-ramping capabilities. This can be costly, because in many instances flexible generators can be more expensive than baseload units that have slower ramping capabilities. If ramping capacity is not available, renewable curtailment may be needed. An alternate solution to this need for ramping is to use energy storage. A question that this raises is how renewable and conventional generators and energy storage would interact in a market environment, and whether certain asset-ownership structures would result in more efficient coordination. To this end, this paper presents a multi-period market-equilibrium model of interactions between these different types of market agents. The impacts on renewable integration of conventional generators having limited ramping capabilities are studied through an illustrative case study. We also examine a variety of structures for the participation of energy storage in the market. We find that co-ownership and co-operation of renewable generators and energy storage brings about the best results from the perspective of alleviating market inefficiencies. Having energy storage directly controlled by the market operator or participating as an independent profit-maximizing firm is less efficient. [ABSTRACT FROM AUTHOR]

In some highly populated areas of the planet, air quality is an issue. Since power plants in those areas significantly contribute to air pollution due to SO 2 and NO x emissions, it is appropriate to operate such power plants enforcing admissible emission limits. Since emissions directly impact the air quality index, which is highly uncertain 24 h in advance, admissible emission limits should be modeled as stochastic parameters.To represent the stochastic nature of such admissible emission limits, we propose a two-stage stochastic programming model for the commitment of power plants. The first stage represents the day-ahead scheduling, and the second stage represents the real-time power system operation under different weather and thus admissible emission limit conditions. We show the effect of stochastic emission limits on power system operations using a simple example and a 118-node case study. [ABSTRACT FROM AUTHOR]

Highlights: [•] A procedure to determine the observability of a power system is developed from an optimization perspective. [•] It determines: the observable islands, the unobservable states, and the unobservable branches. [•] The proposed approach is based on the solution of a well-behaved integer linear optimization problem. [•] The 14-, 30-, 57-, 118-, and 300-bus IEEE systems and the 2383-bus Polish network are analyzed. [•] The proposed technique is more computationally efficient than other traditional numeric-based approaches. [ABSTRACT FROM AUTHOR]