Search

In the electricity market, generation company attempts to maximize their profit in a bidding strategy approach. As the transactions of power and spinning reserve are done in a transmission network, consideration of transmission constraints and spinning reserve uncertainties becomes necessary. In the bidding strategy problem, there are various demand uncertainties. Usually, electricity markets consider a fixed spinning reserve with fixed request probability to ensure that demand is met. However, the actual spinning reserve is stochastic in quantity and requests hours that should be modeled and simulated. Another demand uncertainty is demand response programs include various stochastic types. One of the most famous demand response programs is electric vehicle parking with stochastic charging/discharging amounts and hours. The objection of this study is solving the bidding strategy problem considering transmission constraints, spinning reserve uncertainty, and electric vehicle parking as a demand response program based on a heuristic approach. An actual spinning reserve model using normal distribution is proposed and three case studies are presented. In the first case, improvement in profit of the generation company by 4.15-47.95% and 20.84-31.30% under single and double-sided auctions are reached, respectively. Where transmission constraints and spinning reserve uncertainty are considered, the optimal bidding strategy problem is solved in the energy and spinning reserve market for three-generation companies in the IEEE 6-bus network where transmission constraints are satisfied at all scenarios of spinning reserve requests. When electric vehicle parking is considered, it is shown that demand response programs have direct effects of bidding parameters such as market clearing price, generation companies power awarded and profits. [ABSTRACT FROM AUTHOR]

In this paper, we consider spatially and technologically diversified portfolios of renewable energy resources (RES) on a European scale. These portfolios efficiently allocate wind and solar production capacity with a view on matching load and restricting conventional generation capacity in each country. We investigate the composition of optimal portfolios taking into account the existing cross-border transmission capacity as well as future expansion scenarios. For the purposes of our analysis, we develop several multi-criteria optimization models and apply them to a rich dataset of daily electricity load, RES capacity factors, and transmission capacity constraints for 32 European countries during the period 2010–2015. By exploring different scenarios for the composition of efficient portfolios and cross-border network connectivity, we quantify the effectiveness of the resource aggregation strategy in terms of reducing the energy production deficit or surplus in each country. Empirical evidence shows that the portfolio perspective provides a solid basis for strategic planning on the future expansion of the European grid. [ABSTRACT FROM AUTHOR]

In this paper, we consider spatially and technologically diversified portfolios of renewable energy resources (RES) on a European scale. These portfolios efficiently allocate wind and solar production capacity with a view on matching load and restricting conventional generation capacity in each country. We investigate the composition of optimal portfolios taking into account the existing cross-border transmission capacity as well as future expansion scenarios. For the purposes of our analysis, we develop several multi-criteria optimization models and apply them to a rich dataset of daily electricity load, RES capacity factors, and transmission capacity constraints for 32 European countries during the period 2010–2015. By exploring different scenarios for the composition of efficient portfolios and cross-border network connectivity, we quantify the effectiveness of the resource aggregation strategy in terms of reducing the energy production deficit or surplus in each country. Empirical evidence shows that the portfolio perspective provides a solid basis for strategic planning on the future expansion of the European grid.

Spatially separated locations may differ greatly with respect to their electricity demand, available space, and local weather conditions. Thus, the regions that are best suited to operating wind turbines are often not those where electricity is demanded the most. Optimally, renewable generation facilities are constructed where the maximum generation can be expected. With transmission lines limited in capacity though, it might be economically rational to install renewable power sources in geographically less favourable locations. In this paper, a stochastic bilevel optimisation is developed as a mixed-integer linear programme to find the socially optimal investment decisions for generation expansion in a multi-node system with transmission constraints under an emissions reduction policy. The geographic heterogeneity is captured by using differently skewed distributions as a basis for scenario generation for wind speeds as well as different opportunities to install generation facilities at each node. The results reinforce that binding transmission constraints can greatly decrease total economic and emissions efficiency, implying additional incentives to enhance transmission capacity between the optimal supplier locations and large demand centres.

With the rapid integration of power sources with uncertainty, robustness must be carefully considered in the transmission constrained unit commitment (TCUC) problem. The overall computational complexity of the robust TCUC methods is closely related to the vertex number of the uncertainty set. The vertex number is further associated with: (i) the period number in the scheduling horizon, (ii) the number of nodes with uncertain power injections. In this study, a column merging method (CMM) is proposed to reduce the computational burden by merging the uncertain nodes, while still guaranteeing the robustness of the solution. By the CMM, the transmission constraints are modified, with the parameters obtained based on an analytical solution of a uniform approximation problem, so that the computational time for obtaining the modified constraints is negligible. The CMM is applied under a greedy‐algorithm‐based framework, where the number of merged nodes and the approximation error can be well balanced. The CMM is designed as a preprocessing tool to improve the solution efficiency for robust TCUC problems and is compatible with many solution methods (like two‐stage and multi‐stage robust optimisation methods). Numerical tests show the method is effective. [ABSTRACT FROM AUTHOR]

The processes regarding the generation, transmission and distribution of electricity are gaining interest at global level, due to the constant trend in increasing consumption. Because of this, significant sums are allocated for investments to improve the efficiency of these processes. However, there are a significant number of companies which, by virtue of their small scale and the problems inherent in Latin American economies, have serious difficulties in obtaining several types of equipment, necessary to improve their efficiency. In this context, this paper presents a methodology to operate optimally a small-scale electricity grid. To achieve this goal, two stages are organized; each has its own characteristics and hardware. The first is the resolution of a Master Problem, which solves the problem of committing generators and pre-dispatching loads through a PC. The second stage is the resolution of a Slave Problem, which is responsible for determining the most convenient form of transmission, advocating the maintenance of the system. In this stage the command and operation of the system are also organized; as a consequence, an Embedded System is used that allows the coordination of system actuators. In order to test the method, a system composed of 6 buses and 8 lines is resolved. The results indicate that this proposal operates in efficient times; this allows reducing investment costs due the implemented technology. The application of this methodology in small and medium networks will continue to increase, as new sources of generation increase their participation in these electrical systems. [ABSTRACT FROM AUTHOR]

Community acceptance of renewable energy investments can limit the potential renewable power installation at regional and national level. We use stated preference surveys from Ireland and Germany to parameterise a European power system investment and operation model to determine the impact of public acceptance constraints on system outcomes. The consideration of public acceptance leads to small changes in total costs but large changes in regional results of investment decisions. Transmission investments increase the impact of acceptance constraints on final outcomes, relative to a scenario with no acceptance constraints. The impacts on technology choice, power flows and curtailment are system-specific. • Constraints on public acceptance of wind power included in an energy systems model. • Results for Ireland and Germany are compared. • Acceptance constraints derived from comparable public surveys. • Public acceptance constraints lead to a slight increase in costs and a large deviation in optimal portfolio. • Transmission line investment exacerbates the impact of public acceptance constraints. [ABSTRACT FROM AUTHOR]

This essay performs a reliability constraint stochastic model for unit commitment problem by considering generation and transmission constraints with high wind penetration and volatility of load demands. This query is expressed as a MILP that is based on the linear direct current model. The proposed approach models uncertainty of wind generators output power, load demand fluctuations and stochastic elements outage of the system like generators and transmission lines. In this paper, stochastic interdependence between random variables like wind speed and load demand is recognized. To establish the probability distribution of these correlated random variables, Copula theory is applied. Correlation structure between wind speed of different locations and a group of loads existing in the same area is investigated and studied based on historical data. For representing these uncertainties in the stochastic unit commitment problem, possible scenarios are generated with Monte Carlo simulation method. The reliability constraints are utilized in each scenario to evaluate the feasibility of solutions from a reliability point. The introduced stochastic UC is executed on the RTS 96‐bus test system. Numerical results demonstrate the advantages of implementing stochastic programming on the UC problem by taking into account the intermittent behavior of wind energy and load inconstancy. [ABSTRACT FROM AUTHOR]

Line contingency-constrained dispatch (LCCD) problems are challenging to solve due to the large number of transmission constraints in pre- and post-contingency scenarios, yet the majority of them are never active. This letter proposes a fast method to remove redundant constraints in LCCD problems. The linear programming problems solved in the proposed method are significantly smaller than those in the known approach. [ABSTRACT FROM AUTHOR]

This study investigates the effects of transmission losses, constraints and increased renewable energy penetration on planning energy storage allocation and investment. By modifying a DC optimal power flow model using a linearised approximation for ohmic losses, the authors were able to understand which network characteristic or inhibitor drives the most change in expanding utility‐scale storage. Four different storage technologies were explored: compressed air energy storage, pumped hydro storage, lithium‐ion battery and fly wheel. Each had different charging, capacity and cost characteristics. The results of the storage allocation trials revealed that network congestion was a more influential network inhibitor than were line losses. Losses only had substantial effects on a free‐flowing network but produced marginal changes in allocation in congested ones. The conclusion of the investment trials revealed two things: (i) storage investment is not significantly affected by transmission constraints so long as renewable generation stays constant and relatively low; (ii) more flexible technologies like flywheels are favoured at lower volumes of renewable penetration for their load balancing capabilities while cheaper technologies are best as the volume of renewable power generated increases. [ABSTRACT FROM AUTHOR]

This paper presents a new Mixed Integer Linear Programming model (MILP) to account for the Security-Constrained Unit Commitment Problem (SCUC). Transmission Constraints are introduced through bus balances, line power bound flows, and bus voltage angle differences. Line status is also considered. Binary variables regarding line status (active or inactive) are introduced for this purpose. These variables allow discrete decisions on the connectivity of buses, reducing the angle coupling between buses, with several advantages. Three examples are solved. The results indicate that this method can obtain feasible solutions with CPU times of 2.5 s (for a 6-bus system) and 500 s (for the IEEE 118-bus system), and they reached cost savings up to 4.9% of the total generating cost for one day of programming horizon, in comparison with classical models. Relations of the network are illustrated graphically, and an analysis of the results is presented through new evaluation indexes. [ABSTRACT FROM AUTHOR]

This work presents a new method to evaluate generation reserve margins in systems with renewable sources. In assessing the adequacy of generation reserve amounts, besides failures in generating units, their capacity intermittencies, unavailability, and capacity limits of the transmission system are duly considered. Risk indices are evaluated using quasi-sequential Monte Carlo simulation techniques. The cross-entropy method is used to treat rare events and also to identify critical equipment for operation in each scenario. The proposed method is applied to the original IEEE RTS system and to a modified configuration with insertion of wind power plants. A subsystem of the Brazilian interconnected network is also used to illustrate the practicality of the proposed method. [ABSTRACT FROM AUTHOR]

China’s energy transition, from a coal-dominated system to one with the world largest deployment of wind power, is facing major challenge as China entered the economic “New Normal” in recent years. A key phenomenon is serious wind curtailment, where potential clean wind power is abandoned and the associated environmental and climate benefits could not be fulfilled. Multiple causes have been identified to explain the serious wind curtailment in China; but few studies provide explanations for the patterns of wind curtailment and quantify the relative contributions of the underlying factors. We develop a flexible analytical framework and adapted the logarithmic mean Divisia index method to quantify the contributions of key factors in the change of wind curtailment rate. While the early stage of wind curtailment was constrained by limited transmission capacity, recent wind curtailment is primarily driven by the deceleration of the overall economic growth, which resulted in a slowdown of electricity demand and low willingness-to-accept imported power across provinces. The current economic transition has induced an oversupply of power in general and wind power in particular, especially in wind rich provinces. This seems to be hindering further energy transition. With greater political will and better policy design, the oversupply of power could be an opportunity to speed up substitution of coal with wind and to expedite the de-carbonization of the economy. [ABSTRACT FROM AUTHOR]

This paper presents a new approach for solving Security Constrained Unit Commitment (SCUC) problems based on the application of several linear approximation techniques. Model considers thermal units, transmission constraints, and Pumped Storage Units (PSUs), which are important in power systems during peak and off-peak demand periods. Particularly, the proposed MILP model takes into account the hydraulic heads of PSUs for generating and pumping modes through linear methods of operation point selection. Therefore, a more realistic model is presented in comparison with other models available in the literature. To verify the effectiveness of the proposed approach, a modified IEEE 31-bus power system with two PSUs is tested. Our results indicate that the implementation of PSUs can achieve production cost savings compared with power systems in which thermal generation supplies the entire electricity demand. For improving the power system operations and maintenance, different indicators obtained from results are reported in graphs. This information is useful to identify the most critical parts of the systems and make recommendations for corrective or future actions. [ABSTRACT FROM AUTHOR]

Electric power transmission lines in service in southwest China have fallen far behind the power sources. With rapid development of hydropower in this area, severe water spills for hydropower frequently occur during the flood season because of limited transmission capacity. Effective coordinated operation among large and small hydropower plants (LHPPs and SHPPs) in the area is a feasible approach for alleviating this problem. Using the Yunnan Power Grid (YNPG) of southwest China as an example, a long-term coordinated optimal operation model (COOM) for an integrated system of LHPPs and SHPPs is proposed in this paper. Total electricity that can be consumed or transmitted is defined as electricity absorption. Maximizing the total electricity absorption, which implies balancing high average water heads with reservoir spills, was selected as the objective. Satisfying multiple voltage transmission constraints is a critical issue. A progressive optimality algorithm (POA) was used to solve the model based on a multidimensional search in two-stage optimizations. Results from the YNPG case study show that the proposed method can achieve greater total electricity absorption while effectively reducing spillage. [ABSTRACT FROM AUTHOR]

Electricity prices in an interconnected market are influenced by the occurrence of transmission constraints. Until relatively recently, however, the important effects of transmission constraints on both the trajectory and volatility of electricity prices have not played a large role in empirical models of prices. This paper explores the contribution to price volatility in the Queensland electricity market made by transmission constraints. It is found that robust estimation techniques are necessary to guard against incorrect inference in time series models using electricity price data in which severe price spikes occur. The main empirical lesson is that transmission constraints contribute significantly both to the level and variability of price and consequently the performance of a price forecasting model is likely to be improved by incorporating information on transmission constraints. While the general tenor of this conclusion will come as no surprise, the extent and the importance of these effects found in this paper for forecasting price and for computing summary measures like Value-at-Risk serve as a timely reminder to practitioners. [ABSTRACT FROM AUTHOR]

In this paper, a self-generation scheduling method for a power generation company (GenCo) with renewable generation units is presented. In the proposed method, locational market prices (LMPs) are calculated using the incomplete information on competing market participants by simulating the ISO's market clearing program and considering the effect of physical limitations of transmission lines. The errors associated with forecasted LMP and renewable production are modeled in the GenCo's generation scheduling using a robust optimization approach. The scheduling problem is modeled as a mixed-integer linear programming which is solved by a CPLEX solver in GAMS. An eight-bus system is employed to illustrate the applications of the proposed method. The numerical results show the efficiency of proposed method to reduce the GenCo's financial risks pertaining to uncertain parameters in a competitive electricity market. [ABSTRACT FROM PUBLISHER]

The increasing penetration of wind power makes it a critical problem to maintain the security and economy in power system operations. This study proposes a security constrained economic dispatch (SCED) strategy that considers optimising system state selection and spinning reserve allocation. The system state selection is judged by the criterion of occurrence probability. Considering the impacts of wind power integration on the outage probability of system component, the over‐current protection outage model and voltage protection outage model are employed to calculate the operational outage probabilities of transmission lines and generators, respectively. The fast sorting technique is adopted to select all system states with higher‐occurrence probability than the system security threshold. Besides the traditional constraints on system demand and regulation range, the spinning reserve allocation additionally obeys the transmission constraints in the selected states to avoid transmission congestion in contingency situations. Benders decomposition is utilised to partition the SCED problem into the active power dispatch sub‐problem and the optimal reserve allocation sub‐problem for tractability. A numerical comparison with the traditional N−1 security assessment SCED is given to validate security and economic improvement of the proposed method. [ABSTRACT FROM AUTHOR]

Residual demand curves (RDCs) can be used to represent the strategic interaction of participants in electricity markets. RDCs relate the energy that an agent can buy or sell in one hour with the clearing market price that would be obtained in such hour, assuming the market is organized as simple bid independent auctions. Despite the fact that they have been widely used in the literature, the existence of time and/or spatial constraints in the market clearing algorithm makes the RDCs not directly applicable. This paper tries to overcome these difficulties by extending the concept of RDCs to zonal pricing markets where complex offering conditions and transmission constraints are taken into account. Therefore, the RDCs are redefined in order to capture such effects, which are usually neglected or oversimplified. A new method for computing the redefined RDCs is established and its application to the Iberian electricity market is presented. The results show that modeling complex conditions and transmission constraints in RDCs can have a significant effect when compared to the standard approach found in the literature. Therefore, the method presented in this paper modeling the effect of firm's decisions on market prices in a more accurate way. [ABSTRACT FROM AUTHOR]

The electric vehicles (EVs) can introduce new operation strategies, such as charging management and vehicle-to-grid control, which could provide a considerable level of distributed storage to the power grid. In this paper, a partial decomposition method which is based on the Lagrangian relaxation framework is proposed for the EV charging control in transmission-constrained power systems. The partial decomposition method helps reduce the number of dual multipliers and stabilize the iterative process. The proposed partial decomposition framework is applied to a day-ahead SCUC algorithm which can be easily implemented in the existing hierarchical power system operations. The proposed EV charging control method can not only help reduce the total generation cost of power systems but also alleviate the transmission grid congestion. The feasibility of the proposed method is validated by the case studies applied to the modified IEEE-RTS1979. [ABSTRACT FROM PUBLISHER]

This paper presents a study of large-scale wave energy integration in which transmission constraints are considered. The Vancouver Island electrical grid is considered and is modelled using PLEXOS ® Integrated Energy Model software. The model incorporates the current transmission grid, the existing fleet of main generation stations and ten potential wave farm sites with a total generation capacity of 500 MW. The objectives are to investigate: 1) the potential contribution of wave power toward energy self-sufficiency, 2) the effects of transmission constraints on the viability of alternative wave farm sites, 3) the impacts of wave integration on the load profile. Findings suggest that wave energy integration can significantly reduce the energy dependency on neighbouring jurisdictions but the current grid infrastructure is not adequate to fully support 500 MW of wave power. In this regard, it is shown that potential wave power integration can significantly benefit from transmission expansion for particular pathways. Further, results show that wave integration leads to reductions in the share of energy supplied from other sources and that this reduction follows an annual pattern. This periodic trend is particularly important for a hydro-dominated (energy-limited) grid where water level in reservoirs is managed on monthly and yearly bases. [ABSTRACT FROM AUTHOR]

This paper proposes a methodology to evaluate the spinning reserve requirements of generating systems, taking into consideration the loss of load risk due to failures in the generation and transmission network. In order to provide a more flexible and robust tool to assess the risk indices for bulk power systems, a Monte Carlo simulation based tool is proposed. Due to the rarity of the events associated with this particular problem, an unbiased estimator based on importance sampling is applied, and the set of optimal parameters is obtained using a cross-entropy method. The proposed methodology is capable of assessing the operating reserve requirements, especially the one that is synchronized, to cope with failures and capacity restrictions of both generating and transmission systems. The IEEE-RTS (reliability test system) is used to test the proposed methodology and also some modifications of this system are created to fully verify the ability of the proposed approach to satisfactorily respond to the transmission constraints. [ABSTRACT FROM PUBLISHER]

Spatially separated locations may differ greatly with respect to their electricity demand, available space, and local weather conditions. Thus, the regions that are best suited to operating wind turbines are often not those where electricity is demanded the most. Optimally, renewable generation facilities are constructed where the maximum generation can be expected. With transmission lines limited in capacity though, it might be economically rational to install renewable power sources in geographically less favourable locations. In this paper, a stochastic bilevel optimisation is developed as a mixed-integer linear programme to find the socially optimal investment decisions for generation expansion in a multi-node system with transmission constraints under an emissions reduction policy. The geographic heterogeneity is captured by using differently skewed distributions as a basis for scenario generation for wind speeds as well as different opportunities to install generation facilities at each node. The results reinforce that binding transmission constraints can greatly decrease total economic and emissions efficiency, implying additional incentives to enhance transmission capacity between the optimal supplier locations and large demand centres. [ABSTRACT FROM AUTHOR]

This paper proposes a day-ahead schedule harmonization between wind power plants and concentrating solar thermal power plants having thermal energy storage. The negative correlation between wind power and solar power is computed and an artificial neural network method estimates the power. The schedule is carried out by a bilevel mathematical programming approach. The upper-level determines energy and spinning reserve schedule by the maximization of profit subject to all lower-level problems. Lower-level problems minimize the post-contingency power output. A controllable degree of trust on the schedule is introduced based on n – K security criterion for worst-case contingency. The approach uses duality theory and problem approximations for a conversion into an equivalent mixed-integer linear programming problem. A case study is presented to illustrate the effectiveness of the approach for power producers not only with transmission constraints, but also valuing safekeeping on the day-ahead schedule to ensure a degree trust on the satisfaction of compromises within electricity markets. [ABSTRACT FROM AUTHOR]

During the last decade, energy regulatory policies all over the globe have been influenced by the introduction of competition. In a multi-area deregulated power market, competitive bidding and allocation of energy and reserve is crucial for maintaining performance and reliability. The increased penetration of intermittent renewable generation requires for sufficient allocation of reserve services to maintain security and reliability. As a result the market operators and generating companies are opting for market models for joint energy and reserve dispatch with a cost minimization/profit maximization goal. The joint dispatch (JD) problem is more complex than the traditional economic dispatch (ED) due to the additional constraints like the reserve limits, transmission limits, area power balance, energy-reserve coupling constraints and separate sectional price offer curves for both, energy and reserve. The present work proposes a model for the joint static/dynamic dispatch of energy and reserve in deregulated market for multi-area operation using enhanced versions of particle swarm optimization (PSO) and differential evolution (DE). A parameter automation strategy is employed in the classical PSO and DE algorithms (i) to enhance their search capability; (ii) to avoid premature convergence; and (iii) to maintain a balance between global and local search. The performance of enhanced PSO and DE variants is compared for single/multi-area power systems for static/dynamic operation, taking both linear and non-smooth cost functions. The proposed approach is validated on two test systems for different demands, reserve requirements, tie-line capacities and generator outages. [ABSTRACT FROM AUTHOR]

This paper proposes an analytical solution for bidding strategy problem of transmission-constrained generating companies (GENCOs) in an hour-ahead electricity market. Here, for modeling a bidding strategy problem under a supply function equilibrium model, a bilevel programming (BLP) method is used. In the upper level, the profit of an intended GENCO is maximized, and in the lower level, the independent system operator clears the market via solving a bid-based security-constrained economic dispatch (SCED), in which SCED can be parameterized by GENCOs' bidding strategies. We apply an active-set method to solve a parametric SCED problem. The idea of implementing the active-set method is based on the fact that the optimal solution of a parametric quadratic programming (QP) problem with linear equality constraints can be determined directly. We also decompose generation scheduling, locational marginal prices, and consequently GENCOs' profits, in terms of GENCOs' bidding strategies. In this situation, the BLP transforms to a single-level inequality-constrained optimization, which maximizes GENCO's profit subject to inactive constraints of the lower level. In addition, GENCOs' strategic interactions in oligopolistic marketplace are investigated by using a game theoretic framework, and optimal bidding strategies are determined then by finding the Nash equilibrium of GENCOs' noncooperative competition. Two case studies are provided to illustrate the proposed method; in addition, a numerical method has been conducted to validate the results. The results clarify the impact of each GENCO's bidding strategy on the power flows of transmission lines. [ABSTRACT FROM PUBLISHER]

Considering system uncertainties in developing power system algorithms such as congestion management (CM) are a vital issue in power system analysis and studies. This study proposes a new model for network CM based on chance‐constrained programming (CCP), accounting for the power system uncertainties. In the proposed approach, transmission constraints are taken into account by stochastic rather than deterministic models. The proposed approach considers network uncertainties with a specific level of probability in the optimisation process. Then, single and joint chance‐constrained models are implemented on the stochastic CM. Finally, an analytical approach is used to derive the new model of the stochastic CM. In both models, the stochastic optimisation problem is transformed into an equivalent easy‐to‐solve deterministic problem. Effectiveness of the proposed approach is evaluated by applying the method to the IEEE 30‐bus test system. The results show that the proposed CCP model outperforms the existing models as the analytical solving approach applies fewer approximations and moreover, may have less complexity and computational burden in some special situations. [ABSTRACT FROM AUTHOR]

This paper describes algorithms that use demand-side management to address large-scale integration of wind power. In particular, demand response (DR) is used to manage wind power intermittency by shifting the time that electrical power system loads occur in response to real-time prices and wind availability. An economic dispatch with transmission, DR capacity and operational constraints is used to model the operation of a transmission constrained system with a high penetration of wind power. This optimization model is used to determine the optimal sizing and distribution of DR given a fixed budget for customer incentives and the installation of enabling technology. We demonstrate the effectiveness of the operational model based on a simple PJM 5-bus system and an IEEE 118-bus system. Simulation results show that transmission constraints have a greater effect on sizing of DR capacity than the location of wind power, which means that buses electrically close to congested lines tend to have higher incentives to deploy DR resources than other buses. The second part of the work examines optimal siting of technology that enables DR based on the frequency of DR based load changes, which are generally a function of the network location. [ABSTRACT FROM AUTHOR]

Unsatisfiable transmission constraints (UTCs) may lead to failures in look-ahead dispatch. Hence, the detection of UTCs is a critical issue in power systems, and it may also aid in the discovery of unavailable transmission resources in online look-ahead dispatch. The Lagrangian relaxation (LR) algorithm is a rapid and efficient approach to solving look-ahead dispatch (LD) problems; however, it does not readily handle infeasible problems. Here, we describe criteria and procedures for detecting infeasible LD and for locating and addressing UTCs via an LR framework. The proposed procedures for detecting infeasibility and locating unsatisfiability are incorporated into a conventional LR framework to develop an enhanced LR solution framework without affecting the original computational efficiency. Numerical simulations are carried out using an IEEE 24-bus system, 31-bus system, and several large-scale power systems; the results demonstrate the accuracy, efficiency, and reliability of the methods. [ABSTRACT FROM PUBLISHER]

Many important electricity policy initiatives would directly affect the operation of electric power networks. This paper develops a method for estimating short-run zonal supply curves in transmission-constrained electricity markets that can be implemented quickly by policy analysts with training in statistical methods and with publicly available data. Our model enables analysis of distributional impacts of policies affecting operation of electric power grid. The method uses fuel prices and zonal electric loads to determine piecewise supply curves, identifying zonal electricity price and marginal fuel. We illustrate our methodology by estimating zonal impacts of Pennsylvania's Act 129, an energy efficiency and conservation policy. For most utilities in Pennsylvania, Act 129 would reduce the influence of natural gas on electricity price formation and increase the influence of coal. The total resulted savings would be around 267 million dollars, 82 percent of which would be enjoyed by the customers in Pennsylvania. We also analyze the impacts of imposing a $35/ton tax on carbon dioxide emissions. Our results show that the policy would increase the average prices in PJM by 47–89 percent under different fuel price scenarios in the short run, and would lead to short-run interfuel substitution between natural gas and coal. [ABSTRACT FROM AUTHOR]

Restructured electricity markets may provide opportunities for producers to exercise market power, maintaining prices in excess of competitive levels. This paper proposes a model for generation companies' (GenCos') optimal bidding strategy in a day-ahead oligopoly power market. Cournot model is employed to obtain market equilibrium in which elasticity of demand, transmission security constraints, participants' market power and bilateral contracts are taken into consideration. In order to consider network constraints a multi-period framework is presented to simulate market clearing mechanism in which behaviours of market participants are modelled through piecewise block curves. Subsequently, impacts of bilateral contracts as well as transmission security constraints on participants' characteristics and market clearing prices are studied. A mixed-integer linear programming is employed to solve the problem. Finally a novel multi-objective model is presented for security constrained optimal bidding strategy of GenCos by introducing heuristic effective-supply curves. Consequently, capability of price-maker GenCos to exercise market power and altering market characteristics is shown and impact of bilateral contracts is investigated. A 9-bus IEEE test system is used to implement the simulations and demonstrate the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

This paper is on the self-scheduling for a power producer taking part in day-ahead joint energy and spinning reserve markets and aiming at a short-term coordination of wind power plants with concentrated solar power plants having thermal energy storage. The short-term coordination is formulated as a mixed-integer linear programming problem given as the maximization of profit subjected to technical operation constraints, including the ones related to a transmission line. Probability density functions are used to model the variability of the hourly wind speed and the solar irradiation in regard to a negative correlation. Case studies based on an Iberian Peninsula wind and concentrated solar power plants are presented, providing the optimal energy and spinning reserve for the short-term self-scheduling in order to unveil the coordination benefits and synergies between wind and solar resources. Results and sensitivity analysis are in favour of the coordination, showing an increase on profit, allowing for spinning reserve, reducing the need for curtailment, increasing the transmission line capacity factor. [ABSTRACT FROM AUTHOR]

In this paper, we formulate the economic dispatch problem as a two-stage stochastic convex program for operational decision making under uncertainty. Post-contingency minimum frequency constraints are formulated as convex sets using a simplified model of frequency dynamics. The L-shaped method is used to decompose the large stochastic program into smaller sub-problems. Large-scale system examples are provided to demonstrate the computational efficiency of the proposed model formulation. Numerical results show the value of the proposed model and indicate that the proposed model is especially useful under a higher level of wind penetration. [ABSTRACT FROM PUBLISHER]

In the deregulated electricity market secure operation is an enduring concern of the independent system operator (ISO). For a secure and economical hourly generation schedule of the day ahead market, ISO executes the security constrained unit commitment (SCUC) problem. The objective of the paper is to develop scheduling algorithm using hybrid artificial bee colony algorithm for electric generation that account directly for system security requirements. Utilities can obtain significantly better results if they use optimal scheduling algorithms, which comply directly with security requirements. The proposed SCUC formulation includes constraints such as hourly power demand, system reserves, ramp up/down limits and minimum ON/OFF duration limits. The innovation of our approach is to capture the system security requirements within a network flow sub-problem that is solved in conjunction with the unit commitment problem. To solve such SCUC model a binary coded artificial bee colony (BABC) is used for unit commitment master problem and real coded artificial bee colony algorithm (RABC) is used for the security constrained economic dispatch sub-problem. The effectiveness of the proposed method to solve the SCUC problem is shown on different test systems. [ABSTRACT FROM AUTHOR]

This paper develops models of optimal power flow (OPF) applied to different power systems increasing in complexity; a thermal system without transmission representation, a thermal one with the transmission system, and a hydrothermal system with transmission constraints and a reservoir allowing water management and Peak Load Shaving. This paper develops algebraically the modeling of these features and their effect on optimal dispatch and on short-term energy prices (varying in time and space). This is supported by the use of very simple numerical examples. We conclude that prices are strongly affected by both physical and economical characteristics of the systems. Transmission has a key role here, especially shaping prices over the space and limiting the strategic value of water storage. [ABSTRACT FROM PUBLISHER]

The analysis of the Nash equilibrium (NE) in electricity markets with imperfect competition is subject to two major challenges: the treatment of multiplayer games and the determination of the existence of multiple market equilibria. To resolve these obstacles, a solution method, based on the payoff matrix approach and polynomial equations, is proposed in this paper to calculate all the Nash equilibria of multiplayer games in the electricity markets. First, the proposed method decomposes the game by means of a set of all pure strategies assigned with positive probabilities (support). For each possible support, the NE condition can be characterized by a set of polynomial equations with inequality constraints. Next, the homotopy continuation algorithm is employed to obtain all solutions of the polynomial system. Finally, all Nash equilibria can be found by verifying each solution of all polynomial systems. Two example systems, one involving the Cournot model and the other based on the supply function equilibrium (SFE) model, are applied to test the proposed method, respectively. The results show that the proposed method has the ability to identify all NE under certain conditions, indicating that the proposed algorithm is useful for providing insights into the theoretical analysis of electricity markets. [ABSTRACT FROM PUBLISHER]

In this paper, two different approaches to analyzing firm-based market power considering transmission constraints are proposed. One is an application of the transmission-constrained residual demand Jacobian, while the other is a generalization of the “residual supply index” to the case of transmission constraints. These two approaches provide complementary evaluations of market power. Medium- and large-scale system examples are provided to demonstrate computational efficiency, and both approaches could be applied to real-world electricity markets. [ABSTRACT FROM PUBLISHER]

In this paper, we introduce four new indices of market power in transmission-constrained electricity markets that are based on economic principles together with faithful representation of the effects of Kirchhoff's laws. [ABSTRACT FROM PUBLISHER]