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This paper presents the impact of transmission congestion and demand side management on Electricity Producers’ market power using the quantitative investigation of pool-based market clearing mechanism. The mathematical problem formulation for pool-based market clearing consists of maximization of social welfare objective function, which incorporates maximization of demand benefit obtained from flexible demand bids and minimization of Electricity Producers’ real and reactive power costs obtained from actual and strategic supply bids. In this problem formulation, the nonlinear modeling of power system is used for real and reactive power flow equality constraints, transmission line capability inequality constraints (for incorporating the impact of transmission congestion), and generator capability curve equality constraints. The impacts of demand side management have been incorporated in the problem by considering single-period and multi-period demand flexibilities, and distributed generation placement at appropriate locations. The formulated optimization problem has been solved using nonlinear programming, which provides the nodal prices as byproducts of the solution. The electricity producers’ capability to exercise market power has been analyzed from their revenue and surplus indices, which have been obtained from nodal prices. Various case studies have been simulated on IEEE 30-bus system for quantitative determination of electricity producers’ market power. The results obtained are very interesting and demonstrate demand side strategies to tackle the electricity producers’ market power with actual and strategic bidding under normal and congested system conditions. [ABSTRACT FROM AUTHOR]

This paper explores the allocation process of Financial Transmission Rights (FTRs) in the Italian electricity market. FTRs are financial instruments allowing market participants to hedge against transmission congestion, also playing a critical role in ensuring the efficient use of the transmission system. We present a linear programming (LP) model that simulates the FTRs allocation process, taking into account the transmission capacity limits of the electric network when the total revenue is considered as the utility function. Obtained results highlight that our solution allows us to develop better investment and bidding strategies for optimal transmission management dynamics. In particular, numerical simulations show good results, with an overall MAPE of approximately 7%, indicating that the model accurately predicts the allocation of transmission rights across the network. Overall, the paper provides insights into the inner workings of the FTR allocation process in Italy, also providing improved market efficiency while increasing revenue for market participants. [ABSTRACT FROM AUTHOR]

Hybrid power plants, namely those consisting of variable renewable energy (VRE) generators and energy storage in the same location, are growing in popularity and interact differently with the electrical grid than either component would individually. We investigate plant-grid dynamics in highly congested regions to determine whether stand-alone VRE, stand-alone storage, and hybrid VRE-plus-storage plants will reduce or increase the need for nearby transmission. The focus on congested regions offers empirical insight into future grid conditions, as VRE penetration continues to grow. Near congested load centers, we find that hybrid, stand-alone VRE and stand-alone storage plants each reduce transmission value, defined in terms of production costs. On the other hand, in congested areas with high VRE penetration, stand-alone storage and VRE generators have opposing effects, decreasing and increasing the need for transmission, respectively. Importantly, whether or not a hybrid plant’s optimal operation increases or decreases local transmission value depends on the plant’s technological specifications (i.e., lowering degradation costs of battery cycling reduces transmission value) and regulatory environment (i.e., allowing a hybrid to utilize grid charging reduces transmission value). Therefore, technological advances in energy storage and policy decisions will influence which variation of these results are realized. We also assess the financial implications of transmission expansion on hybrid and stand-alone plants. In VRE-rich areas, we find that wind plants stand to gain significantly more from transmission expansion than do solar plants, with a typical energy market revenue increase equal to that from hybridizing with four hours worth of storage. Results are based on real-time nodal price data and location-specific solar and wind generation profiles for 2018–2021 at 23 existing wind and solar plant locations in the United States that experience congestion patterns representative of regions with either high VRE penetration or high demand.

Because of electricity markets, environmental concerns, transmission constraints, and variable renewable energy sources (VRES), coordinated operation of demand response (DR) and battery energy storage systems (BESS) has become critical. In turn, the optimal coordinated operation of DR and BESS by an entity can affect overall electricity market outcomes and transmission network conditions. The coordinated operation is desirable for the profit-seeking entity, but it may adversely affect the cost and revenues of other market participants or cause system congestion. Though few coordinated operation models already exist, our aim in this research is to provide a novel multi-objective optimization-based methodology for the coordinated operation of DR and BESS to boost market profit. Moreover, another goal is to simultaneously study the combined effects of such coordinated models on transmission networks and electricity markets for the first time. This paper has proposed a new method for coordinated DR and BESS utilization by a load-serving entity (LSE) to increase its profit. Moreover, it has employed agent-based modeling of the electricity systems (AMES) for testing our coordinated DR and BESS method under day-ahead market and transmission system conditions. Simulation results of case studies indicate that the operating costs of all LSEs decreased, and there was as much as 98,260 $/day in cost savings for BESS deploying LSE1. Although revenues of cheaper generation companies (GenCos) decreased, those of expensive GenCos increased or showed mixed trends. For example, GenCo 3 exhibits an 8765 $/day decrease in revenue for 25% BESS capacity, whereas a 6328 $/day increase in revenue for 37.5% BESS capacity. The variance of LMPs, widely used as a risk index, greatly decreased for the LSE utilizing the coordinated methodology, somewhat decreased for other LSEs but increased for cheaper GenCos with no LSE at the local node. Since BESS deployment decisions of an LSE can have system-wide or market-wide consequences, simulation analysis before deployment can help reduce market distortions or system congestions. [ABSTRACT FROM AUTHOR]

With the rapid development of high-efficiency, long-distance, and large-capacity power interaction in multiple communities, prosumers in each community who can participate in three markets, i.e., green power market, electricity market and carbon market, may make decisions based on incomplete rational behaviors. The behaviors, e.g., purchasing plenty of power from the power plants through the independent system operator (ISO) at a certain time slot, may cause the problem that a certain power line cannot transmit the power since the amount of power intended to transmit via the power line is beyond the constraint of the physical network, which is regarded as the transmission congestion. How to realize the optimization of energy management for the prosumers and power plants in three markets considering transmission congestion arouse the public concern. In this paper, an optimal energy management method is proposed for the power plants and prosumers with community energy storage considering transmission congestion based on carbon emission flow. It is constructed with a three-level structure, i.e., prosumer level, ISO level and power plant level. At the first level, i.e., prosumer level, based on the cumulative prospect theory, an incomplete rational behavior model is developed for the prosumers who can store the excess power in community energy storage for backup. Due to the existing prosumer peer-to-peer energy trading approach, all prosumers in the same community can be aggregated into a community agent to participate in the three markets, which can deliver the power demand from the prosumers to the ISO at the second level. At the third level, power plant level, two energy trading models of power plants are established, which can deliver the power supply from the power plants to the ISO at the second level, i.e., ISO level, as well. One is presented for the coal-fired power plants according to the cost-benefit function theory, the other one is constructed for the renewable power plants considering the uncertainty of renewable output power. Then, at the second level, an energy management method considering transmission congestion is developed in respect of the power demand from the first-level behavior and the power supply from the third level. Finally, the optimization of energy management is solved under the Lagrange multiplier method with the improved differential evolution algorithm, which is verified in numerical simulations with the effectiveness of the proposed method. • A novel method based on cumulative prospect theory for the incomplete rational behaviors of prosumers is proposed. • A three-level energy management structure is constructed for the prosumers with community energy storage. • An optimal energy management strategy is proposed based on the improved differential evolution algorithm. [ABSTRACT FROM AUTHOR]

Transmission congestion identification and expansion planning methods for power systems with wind power are proposed in this paper. First, wind farm model is established based on Copula theory considering the uncertainty and correlation of wind speed and wind turbine’s failure. Next, generation rescheduling model with wind farm and TCSC is established and a transmission congestion identification method based on reliability evaluation is proposed. Then, alternative schemes of expansion planning have been proposed according to the results of congestion identification, and the final scheme is determined by probabilistic economic analysis method. Finally, case studies have been carried out on modified RBTS test system. For the mRBTS system, when wind farms are integrated into system from bus 1 and 2, the best expansion solution is to build new transmission line; while when wind farms are integrated from bus 3 and 5, the best expansion solution is to install TCSC. Results of case studies verify the effectiveness of the proposed congestion identification and expansion planning methods of transmission system, and reveal that the location of wind power affects the decision making of transmission system expansion planning scheme.

Summary: In this paper, a novel method is presented to study the impact of the most congested line on Locational Marginal Price (LMP), and Transmission Congestion Cost (TCC). In most congested condition, the abrupt difference in LMP between two nodes result in enormous TCC and create a huge loss for participants. The method developed in this paper is to find the most congested line on the basis of TCC value, and its sensitivity on the overall network is thoroughly studied. The LMPs computed at each node help in grouping the nodes or buses into two parts, namely, zone 1 or congested zone and zone 2 or non‐congested zone. This method helps market operators to analyze the impact of the most congested line. In addition, the method is handled by obtaining the exact location and sizing of Distributed Generation (DG). Moreover, to formulate the problem accurately, the proposed method is based on AC optimal power flow (ACOPF), which includes network losses in contrast with the lossless DCOPF. The MATLAB interior point method is used to solve the proposed method, and it is tested on IEEE Reliability Test System (IEEE‐RTS) 24 bus and IEEE 30 bus system network in different operating conditions. The results thus obtained shows the betterment of the proposed method. [ABSTRACT FROM AUTHOR]

The fluctuation of energy price and the expensive energy cost during the peak period will affect the living cost of users, and transmission congestion will also bring additional transmission costs. To solve this problem, this paper establishes the demand response model of energy users. Users can obtain a lower energy price by adjusting their replaceable load and price load. This paper uses cooperative game pricing method to solve the problem of congestion cost allocation. In order to ensure the privacy and independence of the energy system, the alternating direction method of multipliers is proposed to decouple the integrated energy system. The model is verified in the integrated energy system of IEEE 14-node electricity system and 14-node natural gas system. Simulation results show that the algorithm has convergence and can reduce the cost of user participation in demand response. • ADMM divides IES into two subsystems: electricity system and natural gas system. • Shapley value method is used to allocate congestion cost and improve social welfare. • Users participate in demand response to alleviate transmission congestion. • Consider the discomfort cost of energy use, making the user cost more accurate. [ABSTRACT FROM AUTHOR]

This paper offers a Multi-objective Improved Differential Evolutionary algorithm (MOIDEA) for ultimate allocation of Modular Multilevel Converter based totally Unified power glide Controllers (MMC-UPFC). MMC-UPFC is the most convenient facts own family device suitable for coping with both congestion and voltage profiles as we manage the electricity markets. The maximum appropriate place for solving the MMCUPFC is identified primarily based on identifying the weakest bus in the modern-day system. To optimize multi-goal function, the most effective goal and length are diagnosed by using the Multi-goal improved Differential Evolutionary algorithm (MOIDEA), which is made of a spread of things that outline device security, voltage deviation, system overload, and actual electricity loss. The proposed paintings might be implemented on a MAT-LAB / simulation platform, and the effectiveness of the proposed scheme tested the use of the IEEE 30 bus system is compared with techniques. [ABSTRACT FROM AUTHOR]

Security constraint is a key component in unit commitment to guarantee reliable generator commitment. Large set of security constraints are notorious for making the problem difficult to solve. Constraint screening, i.e., filtering out non-dominating constraints, is regarded as a powerful tool to address this challenge. This work presents a security-constraint screening that can effectively integrate virtual transaction and capture changes online in real-time or look-ahead markets. The proposed approach takes advantage of both deterministic and statistical methods, which leverages mathematical modeling and historical data. Effectiveness are verified using Midcontinent Independent System Operator (MISO) data. [ABSTRACT FROM AUTHOR]

The insufficient power system flexibility and transmission congestion are two fundamental reasons for wind power curtailment. As the scale of the wind power in the power system is growing rapidly, the two factors of wind power curtailment events coexist and have a certain coupling relationship. The configuration of the energy storage system can not only increase the flexibility of the system but also alleviate transmission congestion. Therefore, the joint planning of energy storage and transmission grid that takes into account the flexibility of the system and the transmission congestion is of great significance to solve the wind curtailment. Hence, this paper first decouples the insufficient flexibility and transmission congestion wind power curtailment, and quantitatively analyzes the impact of transmission capacity on the coupling relationship between the two; second, reveals the principle of joint planning of energy storage system and transmission congestion, and constructs an optimization model, and proposes to set up the capacity of the wind power which connects the power network. The solution procedure, which deals with grid planning and the energy storage system optimization in turn, not only ensures the accuracy of the model, but also significantly reduces the calculation cost. Finally, a case study of a wind power base in Northeast China and an improved Garver-6 system are carried out to verify the effectiveness of the proposed method.

Hydroelectric and thermal generation provides a safe and regular operation in front of load shape. However, changes in the expansion policy that includes wind and solar power plants modify the path of transmission power flow and load response requirements. In order to accommodate these new renewable sources, long-term systemic approach is complex and should be based on hydrothermal optimization and load shape allocation. Recent studies show that these resources are not sufficient for the systemic needs. Energy storage can be an alternative to the regularization of renewable sources. The problem is to scale this equipment in a long-term scenario in a suitable way so as to charge a fair price for the service. At this point, operational reserve is fundamental, but often overlooked in hydrothermal expansion models. Current expansion planning models cannot adequately meet the new system demands, but they are critical for determining long-term prices, so they must be modernized and replaced gradually to avoid further problems. The paper proposes a transition approach from the current modeling through the inclusion of a new step in the expansion planning process, called the operational reserve analysis, which seeks to meet the load–generation balance criteria and to scale, if necessary, the use of additional storage devices. It is also proposed a power flow analysis to find optimal location in transmission grid, in addition to evaluating different storage devices needs and how transmission network expansion with multiple AC and DC that provides greater benefits to interconnected system. To validate proposed algorithm application, a case study was carried out for IEEE 14 bus database, and a real application was carried out for the Brazilian system, considering up-to-date planning tools and hourly allocation in load curves. This case demonstrates that the method is suitable as transition for new planning tools. [ABSTRACT FROM AUTHOR]

The limited reserve of fossil fuels and public awareness of environmental issues prompt the rapid development of renewable energy generation. However, the centralised utilisation of renewable energy in bulk power systems is impeded mainly by its volatile nature and transmission congestion, leading to the spillage of renewable power. The energy storage unit is expected to be a promising measure to smooth the output of renewable plants and reduce the curtailment rate. This study addresses the energy storage sizing problem in bulk power systems. To capture the operating status of the power system more accurately, the authors use a dedicated power flow model which involves voltage and reactive power. The uncertainty of renewable generation is described via inexact probability distributions encapsulated in a data‐driven Wasserstein‐metric based ambiguity set, based on which the renewable energy curtailment rate is formulated as a distributionally robust chance constraint. The objective is to minimise the total investment cost, and the optimal sizing problem gives rise to a distributionally robust chance‐constrained program, and is reformulated as a tractable linear program via conservative approximation. Case studies conducted on the modified IEEE 30‐bus and 118‐bus systems demonstrate the effectiveness and performance of the proposed approach. [ABSTRACT FROM AUTHOR]

Load Redistribution (LR) attacks operate by adversarial manipulations of load measurements in electrical power grids. Machine Learning (ML) based detectors have been effective to detect LR attacks where the attacked load vectors are outliers of the distribution of normal load vectors. On the other hand a recent family of attacks, known as dummy LR attacks , are capable of bypassing the ML-based detectors by choosing attack load vectors that are not outliers. This paper presents an approach for defense against such stealthy third-generation dummy LR attacks by identifying and monitoring critical loads. A case study on the IEEE 30 bus system demonstrates the effectiveness of the proposed approach for defense against dummy LR attacks. A larger case study on actual load and line power flow measurements of the Delhi Power Grid demonstrates that the proposed approach can be extended to real-world grids. • Conversion of formulation of dummy LR attack into ML classification problem. • Complete defense against dummy LR attack by protecting critical loads. • Extension of the proposed approach to the real-world grids. • Comparison with the existing approaches to demonstrate the superiority. [ABSTRACT FROM AUTHOR]

Abstract Transmission congestion management became a grievous issue with the increase of competitiveness in the power systems. Competitiveness arises due to restructuring of the utilities along with the penetration of auxiliary services. The present study depicts a multi objective technique for achieving the optimal capacities of distributed generators (DG) such as solar, wind and biomass in order to relieve congestion in the transmission lines. Objectives like transmission congestion, real power loss, voltages and investment costs are considered to improve the technical and economical performances of the network. Multi objective particle swarm optimization algorithm is utilized to achieve the optimal sizes of unity power factor DG units. The insisted methodology is practiced on IEEE-30 and IEEE-118 bus systems to check the practical feasibility. The results of the proposed approach are compared with the genetic algorithm for both single and multi-objective cases. Results revealed that the intimated method can aid independent system operator to remove the burden from lines in the contingency conditions in an optimal manner along with the improvement in voltages and a reduction in real power losses of the network.

A bulk power system is conventionally characterized by a complex structure with a large number of components. Each component generally has a different contribution to the transmission congestion (TC) of a system. Thus, a TC sharing method that can be used to evaluate the contribution of each component to the system TC and recognize the weak parts from the perspective of TC should be built. This paper presents a transmission congestion tracing (TCT) principle based on the failed component sharing principle and proportional sharing principle and a TCT model using the Monte Carlo simulation method. Case studies on the IEEE Reliability Test System indicate that the proposed method is effective and feasible.

The literature suggests that, in short‑term electricity‑price forecasting, a combination of ARIMA and support vector regression (SVR) yields performance improvement over separate use of each method. The objective of the research is to investigate the circumstances under which these hybrid models are superior for day‑ahead hourly price forecasting. Analysis of the Nord Pool market with 16 interconnected areas and 6 investigated monthly periods allows not only for a considerable level of generalizability but also for assessment of the effect of transmission congestion since this causes differences in prices between the Nord Pool areas. The paper finds that SVR, SVRARIMA and ARIMASVR provide similar performance, at the same time, hybrid methods outperform single models in terms of RMSE in 98 % of investigated time series. Furthermore, it seems that higher flexibility of hybrid models improves modeling of price spikes at a slight cost of imprecision during steady periods. Lastly, superiority of hybrid models is pronounced under transmission congestions, measured as first and second moments of the electricity price.

The uncertainty of generation and load increases in the transmission network in the power market. Considering the transmission congestion risk caused by various uncertainties of the transmission network, the optimal operation strategies of the transmission network under various operational scenarios are decided, aiming for the maximum of social benefit for the evaluation of the degree of scenario congestion. Then, a screening method for major congestion scenario is proposed based on the shadow price theory. With the goal of maximizing the difference between the social benefits and the investment and maintenance costs of transmission lines under major congestion scenarios, a multi-stage transmission network planning model based on major congestion scenarios is proposed to determine the configuration of transmission lines in each planning stage. In this paper, the multi-stage transmission network planning is a mixed integer linear programming problem. The DC power flow model and the commercial optimization software CPLEX are applied to solve the problem to obtain the planning scheme. The improved six-node Garver power system and the simplified 25-node power system of Zhejiang Province, China are used to verify the effectiveness of the proposed multi-stage planning model.

This study proposes a method to evaluate the impact of transmission congestion on the flexibility of a power system, based on the ramping capability shortage expectation (RSE). Here, flexibility refers to the ability to retain a power balance in response to changes in the net load. The flexibility issue arises due to the extensive integration of renewable energy resources; specifically, the higher the degree of integration, and the greater the variability and uncertainty in the power system. Flexibility is further limited by the net transfer capacity (NTC) of transmission lines. Here, we propose a method capable of capturing the extent to which transmission congestion affects the power system, to identify transmission reinforcement options for improved flexibility. In Korea, transmission congestion occurs frequently in regions to the north and southeast. A case study for a Korean power system in 2030 was conducted. Simulation results showed that the impact of transmission reinforcement in flexibility tends to be proportional to the NTC and is greater when the penetration level is low.

This work explores the impact of the rapid growth of plug-in electric vehicles on wholesale electricity pricing. Understanding electric vehicle impacts on the grid is important for the mid- and long-range planning of transmission owners, distribution utilities, and regional system operators. Current research in electric vehicles considers technology adoption projections and the infrastructure needed to support electric vehicle growth. This work considers how projected electric vehicle growth in the State of Wisconsin would impact the transmission congestion and wholesale electricity pricing in the year 2030. We find minimal impacts on electricity prices (

This paper addresses the problem of how best to coordinate, or “stack,” energy storage services in systems that lack centralized markets. Specifically, its focus is on how to coordinate transmission-level congestion relief with local, distribution-level objectives. We describe and demonstrate a unified communication and optimization framework for performing this coordination. The congestion relief problem formulation employs a weighted ${\ell _{1}\text{-norm}}$ objective. This approach determines a set of corrective actions, i.e., energy storage injections and conventional generation adjustments, that minimize the required deviations from a planned schedule. To exercise this coordination framework, we present two case studies. The first is based on a 3-bus test system, and the second on a realistic representation of the Pacific Northwest region of the United States. The results indicate that the scheduling methodology provides congestion relief, cost savings, and improved renewable energy integration. The large-scale case study informed the design of a live demonstration carried out in partnership with the University of Washington, Doosan GridTech, Snohomish County PUD, and the Bonneville Power Administration. The goal of the demonstration was to test the feasibility of the scheduling framework in a production environment with real-world energy storage assets. The demonstration results were consistent with computational simulations. [ABSTRACT FROM AUTHOR]

In this paper, a multiobjective-based congestion management (CM) methodology is proposed using the optimal transmission switching (OTS) strategies considering the minimization of the total operating cost and the maximization of probabilistic reliability as two conflicting objectives. To improve the efficiency of the transmission network, this paper considers the possibility of changing the topology of the transmission system using the transmission switching strategy. The OTS can increase the economic efficiency of power dispatch with the existing infrastructure. The objective of OTS is to find the most influential lines as candidate lines for the disconnection. System operators can change the network topology to increase the transfer capacity, to improve voltage profiles, and also to improve the reliability of the power system. In this paper, the loss of load probability reliability index is used to indicate the probability that an electrical power system is not able to serve the prescribed load within the concerned period of time. Here, the transmission lines that should be switched are determined by using the security-constrained ac optimal power flow. The proposed optimization problem is solved using the hybrid of evolutionary and stochastic programming approaches. The output of the proposed OTS is to find the transmission lines that have to be switched so that the congestion in the system can be relieved. A multiobjective optimization approach is used to generate the Pareto optimal solutions. The suitability and effectiveness of the proposed CM approach have been examined on IEEE 30- and 300-bus test systems. [ABSTRACT FROM AUTHOR]

Transmission Congestion creates hindrance that limit the most economical supply to reach demands. Hence, it is relieved at the earliest to make optimum utilization of available transmission network in order to achieve maximum profits. In this work, optimal capacities of distributed generation (DG) units are inserted to remove the congestion in the transmission lines of bulk power system. Multi-objectives like real power losses, investment costs, voltage deviations and line capacities are converted into single objective and is minimized to obtain the optimal capacities of the DG units. Flower Pollination Algorithm (FPA) is implemented to achieve the best capacities of the DGs that are operating at unity (UPF) and 0.9 lagging power factors. The capacities of DGs are obtained at multiple locations instead of single optimal or sub-optimal location in order to improve the practical feasibility while connecting the DGs. The proposed methodology is practiced on IEEE 30 and 118 bus system to check the effectiveness. Further, the result obtained by FPA are compared with Genetic algorithm (GA) and Particle Swarm Optimization (PSO) approaches in terms of real power losses ( RPL ) and line flows. Results conveyed that the proposed algorithm had superior features, stable convergence characteristics and good computational efficiency. [ABSTRACT FROM AUTHOR]

In the liberalised electricity market, congestion management (CM) with a high penetration of wind energy is a challenging task for Independent System Operators (ISOs). Even though demand response (DR) provides an opportunity to alleviate transmission congestion, strategic selection of aggregated loads to contract with for DR in the day‐ahead economic dispatch is still under‐investigated. To solve this problem, this study proposes a bi‐level optimisation model to determine the optimal DR buses for CM in the day‐ahead market considering the uncertainty of wind power. The upper model serves to compute the available transfer capability (ATC), while the lower model is to calculate the stochastic dynamic optimal power flow. Through converting the stochastic ATC values to a summation of load supply capability of each load node, the loads which will deteriorate transmission congestion if the corresponding demand grows are determined. The corresponding loads with DR are selected as the optimal candidates. Furthermore, this study constructs a two‐stage optimisation model for optimal load dispatch by incorporating both price‐based DR and incentive‐based DR. The result can be used to assist system operators in decision‐making of electricity biddings from DRs for load curtailment and shift in the market clearing. As a result, the difference of peak and valley loads is reduced; ISOs as well as DR participators can both get economic benefits. Simulation results from the PJM 5‐bus training system and the modified IEEE 30‐bus system demonstrate the effectiveness of the proposed algorithm for DR contracting in CM. [ABSTRACT FROM AUTHOR]

Power system is facing new challenges and opportunities in the environment of the internet of things. Under the circumstance of Internet of things, the transmission congestion management of interruptible load is the important measure to improve system reliability and operating economy. Considering the condition of target selected under different circumstances, this paper proposes a new multi-objective model of transmission congestion management with interruptible load based on brand circuit overload match with interrupt capacity. The multi-object model puts forward three goals, brand circuit overload match with interruptible load, the minimum number of interruptible load nodes and the minimum total interruption of interruptible load. Against other optimization methods can not prioritize to multiple targets and it can easily lead to convergence in the process of solving problems, the paper presents construct evaluation function based on the linear weighted sum to optimize multi-objective linear problem. This method can be sorted prior to multi-objective optimization model. And it has better convergence than other optimization methods in the solution process. Finally, it tests and verifies the correctness of method through the IEEE 30 bus power system. And it successfully applied to grid congestion management in oil. [ABSTRACT FROM AUTHOR]

Power losses in transmission systems are most of the time seen as a small amount (2–5%) from the total power carried over the network. However, transmission loss allocation may play a crucial role in the congestion management of balancing markets. Several loss allocation and loss cost allocation methods have been proposed and implemented in different markets. Most of these methods are intended to be used post‐market. This study presents a novel near‐real‐time loss allocation method suitable to be applied in real‐time markets as envisioned in the smart grid context. The proposed method allocates the total system losses fairly to all the demand and generator buses, by taking into account the dynamical changes of the power network states, which are provided by an accurate state estimation. The power flows required to determine the allocated losses are executed after an economic dispatch procedure that ignores losses, making the choice of the slack bus an independent factor. The proposed method can be used as an accurate real‐time pricing for network usage while the resolving of transmission congestion can be more reliable. [ABSTRACT FROM AUTHOR]

The transmission system expansion planning process requires lots of calculations looking many years into the future, and the results are based on assumed load growth. If the load growth assumed in the planning process is not correct and unexpected load growth occurs for some load points, the transmission system could face serious congestion and even overloading problems. In this paper, transmission line impedance adjustment techniques using distributed series reactance (DSR) is considered. The DSRs can be used to control power flow and alleviate overloading problems. A new term, DSR congestion relief factor, is introduced. The DSR congestion relief factor measures the increase of transmission line capacity with the application of DSRs. Parametric studies run on the IEEE 39-bus system are presented. These studies investigate the economic benefits of DSRs and the use of DSRs for single contingencies and compare DSRs with existing technologies for expanding the transmission system. [ABSTRACT FROM AUTHOR]

The literature suggests that, in short-term electricity-price forecasting, a combination of ARIMA and support vector regression (SVR) yields performance improvement over separate use of each method. The objective of the research is to investigate the circumstances under which these hybrid models are superior for day-ahead hourly price forecasting. Analysis of the Nord Pool market with 16 interconnected areas and 6 investigated monthly periods allows not only for a considerable level of generalizability but also for assessment of the effect of transmission congestion since this causes differences in prices between the Nord Pool areas. The paper finds that SVR, SVRARIMA and ARIMASVR provide similar performance, at the same time, hybrid methods outperform single models in terms of RMSE in 98 % of investigated time series. Furthermore, it seems that higher flexibility of hybrid models improves modeling of price spikes at a slight cost of imprecision during steady periods. Lastly, superiority of hybrid models is pronounced under transmission congestions, measured as first and second moments of the electricity price. [ABSTRACT FROM AUTHOR]

An efficient, low-cost and reliable operation of a power system by adjusting the available electricity generation resources to supply demand of the system is required to ensure satisfied economic plant dispatching. This paper proposes a scheme for economic redispatch model considering the transmission issues such as transmission congestion and network losses, in order to obtain an optimal energy price in supporting competitive electricity market under deregulated environment of a power system. [ABSTRACT FROM AUTHOR]

Greater penetrations of variable renewable generation on some electric grids have resulted in increased levels of curtailment in recent years. Studies of renewable energy grid integration have found that curtailment levels may grow as the penetration of wind and solar energy generation increases. This paper reviews international experience with curtailment of wind and solar energy on bulk power systems in recent years, with a focus on eleven countries in Europe, North America, and Asia. It examines levels of curtailment, the causes of curtailment, curtailment methods and use of market-based dispatch, as well as operational, institutional, and other changes that are being made to reduce renewable energy curtailment. [ABSTRACT FROM AUTHOR]

This study presents gravitational search algorithm (GSA)‐based optimisation technique for the optimum co‐ordination of multi‐type flexible AC transmission system (FACTS) devices with the existing reactive power sources present in a connected power network. Three types of FACTS devices: namely, unified power flow controller, static var compensator and thyristor controlled series capacitor are used in the present problem and IEEE‐30 and IEEE‐57 bus systems are taken as standard test systems. The existing reactive power sources are reactive generations of the generators and transformer tap setting arrangements. The main objective of the present study is to reduce active power loss, system operating cost including the cost of FACTS devices and congestion in transmission network. The result obtained with the proposed GSA‐based approach is compared with the results obtained by genetic algorithm, differential evolution and particle swarm optimisation techniques. It is observed that by optimal placement of FACTS devices along with the optimal setting of the transformer tap arrangements and control of reactive generations of the generators line loss, system operating cost and transmission congestion is reduced significantly by the GSA‐based method compared with other methods. [ABSTRACT FROM AUTHOR]

Transmission congestion is one of the most challenging issues in operation of deregulated electricity markets of today. This study investigates into benefit of unified power flow controller (UPFC) as a viable solution to the problem of congestion management. A comprehensive load flow model for the UPFC is presented which has the capability to control active and reactive powers and voltage magnitude simultaneously. This study discusses about two different approaches for determination of suitable location of UPFC. One of them is sensitivity‐based approach and the other is pricing‐based method. Reduction in total system losses has been taken as objective in sensitivity‐based approach. Furthermore, sensitivity of total system losses with respect to control parameters of UPFC have been evaluated using two different methods. A pricing‐based approach which makes use of locational marginal price difference and congestion rent for determination of suitable location of UPFC. Minimisation of cost of rescheduling subjected to system operating constraints is the objective to be met for congestion management. Interior point method, a mathematical tool for non‐linear optimisation, is used to obtain the solution of above‐mentioned problem. [ABSTRACT FROM AUTHOR]

Analysis of market power in electricity markets is relevant for understanding the competitive development of the industry's restructuring and liberalization process, but in the existing literature, there is not an adequate consideration of line transmission congestion. The aim of this paper is to propose a new approach to measuring market power in the Italian Power Exchange (IPEX), explicitly considering transmission line congestion. We construct a new measure of the residual demand curve to disentangle unilateral market power from congestion rent for the main Italian generators during the period April 2004 to December 2007. In Italy, this period was one of stable transmission network structure. Following the approach of Wolak (2003, 2009), we measure the unilateral market power with the Lerner index (LI), computed as the inverse of the residual demand elasticity. In conclusion, the correct modeling of the residual demand curve including transmission congestions enables us to compute the zonal LI and therefore more accurately measure the market power when congestion occurs. Our results show that various generators exercise market power only in specific zones. These findings provide deeper understanding of market outcomes in the presence of congestion, suggesting appropriate policy directions for market surveillance and competition regulation. [ABSTRACT FROM AUTHOR]