LMP Congestion Management Using Enhanced STF-LODF in Deregulated Power System

Nowadays electricity market industry is become a major impact on power system for privatization and deregulation of power in global wise. As per the limitation of the transmission system, the complexity arises and the supply fact will be with demand at the time of balancing. The congestion of the power system is occurring based on the transmission limits of the power desire and amount for operating the system. In order to avoid transmission line congestion, enhanced STF-LODF method is proposed. It shows the regulated line transmission flow with generating units by implementing renewable energy resources (RER) based on enhanced STF-LODF in power systems. It avoids the congestion of transmission line frequently in the power system and manages price based on pricing and sensitivity approach, and also manages optimal location of congestion transmission and instability issues of voltage. The congestion management of Locational Marginal Pricing (LMP) is performed with minimum line loss, less cost, line flow, better sensitivity and better performances in optimal power flow and control flow. The efficiency of the proposed power system is analyzed and verified by the simulation results of tested IEEE 14 bus system.

An Efficient Hybrid TLBO-PSO Approach for Congestion Management Employing Real Power Generation Rescheduling

In the present deregulated electricity market, power system congestion is the main complication that an independent system operator (ISO) faces on a regular basis. Transmission line congestion trigger serious problems for smooth functioning in restructured power system causing an increase in the cost of transmission hence affecting market efficiency. Thus, it is of utmost importance for the investigation of various techniques in order to relieve congestion in the transmission network. Generation rescheduling is one of the most efficacious techniques to do away with the problem of congestion. For optimizing the congestion cost, this work suggests a hybrid optimization based on two effective algorithms viz Teaching learning-based optimization (TLBO) algorithm and Particle swarm optimization (PSO) algorithm. For binding the constraints, the traditional penalty function technique is incorporated. Modified IEEE 30-bus test system and modified IEEE 57-bus test system are used to inspect the usefulness of the suggested methodology.

Two-time-scale Congestion Management Scheme for Microgrid Integrated Distribution Networks

Microgrids(MGs)with high penetration of dis-tributed generators may cause congestion in the distribution net-work during operation.To address this issue,this paper proposes a two-time-scale congestion management scheme for multiple MGs integrated distribution networks.Day-ahead hourly-scale dynamic congestion management(DCM)is formulated as a con-strained optimization problem,which can be solved by utilizing the proposed alternating iterative method,with the privacy of both the distribution network and MGs being preserved.The sub-hourly-scale contract energy tracking aims at fully utilizing the controllable resources of the MGs to minimize the difference of the contract and actual exchanged energy between the MG and distribution network.Through coordination of the proposed two timescales of management schemes,the MGs integrated distribution networks can operate economically while avoiding the probable congestion predicament with high penetration of renewable energy.Simulation studies with a i3-bus system MGs integrated distribution network demonstrated this proposed approach is effective to manage the congestion problem in the distribution network,while the energy tracking approach can improve the welfare of the MGs engaged in energy contracts execution.IndexTerms-Alternating giterativemethod,congestion management,microgrids,renewable energy integration.

Congestion management with demand response considering uncertainties of distributed generation outputs and market prices

In recent years,much attention has been devoted to the development and applications of smart grid technologies,with special emphasis on flexible resources such as distributed generations(DGs),energy storages,active loads,and electric vehicles(EVs).Demand response(DR) is expected to be an effective means for accommodating the integration of renewable energy generations and mitigating their power output fluctuations.Despite their potential contributions to power system secure and economic operation,uncoordinated operations of these flexible resources may result in unexpected congestions in the distribution system concerned.In addition,the behaviors and impacts of flexible resources are normally highly uncertain and complex in deregulated electricity market environments.In this context,this paper aims to propose a DR based congestion management strategy for smart distribution systems.The general framework and procedures for distribution congestion management is first presented.A bi-level optimization model for the day-ahead congestion management based on the proposed framework is established.Subsequently,the robust optimization approach is introduced to alleviate negative impacts introduced by the uncertainties of DG power outputs and market prices.The economic efficiency and robustness of the proposed congestion management strategy is demonstrated by an actual 0.4 kV distribution system in Denmark.

An approach to Iocational marginal price based zonal congestion management in deregulated electricity market

Congestion of transmission line is a vital issue and its management pose a technical challenge in power system deregulation. Congestion occurs in deregulated electricity market when transmission capacity is not sufficient to simultaneously accommodate all constraints of power transmission through a line. Therefore, to manage congestion, a locational marginal price （LMP） based zonal congestion management approach in a deregulated elec- tricity market has been proposed in this paper. As LMP is an economic indicator and its difference between two buses across a transmission line provides the measure of the degree of congestion, therefore, it is efficiently and reliably used in deregulated electricity market for conges- tion management. This paper utilizes the difference of LMP across a transmission line to categorize various congestion zones in the system. After the identification of congestion zones, distributed generation is optimally placed in most congestion sensitive zones using LMP difference in order to manage congestion. The performance of the proposed methodology has been tested on the IEEE 14-bus system and IEEE 57-bus system.

Demand response based congestion management in a mix of pool and bilateral electricity market model

The independent system operator （ISO） is a key element in the deregulated structure with one of the responsibilities of transmission congestion management （CM）. The ISO opts market based solutions to manage congestion receiving bids from generation companies （GENCOs） as well as distribution companies （DISCOMs） to reschedule their generation and relocate demand. The nodal prices increases during the congestion hours and the demand response to nodal prices will be an effective tool for the control of congestion. In this paper, demand response-based CM has been proposed for a mix of pool and bilateral electricity market model. The linear bid curves have been considered for demand bids to respond to the congestion in the network. The bilateral demand has been obtained with minimum deviations in their preferred schedule. The impact of flexible alternating current transmission system （FACTS） devices viz static var compensator （SVC） and thyristor controlled series compensator （TCSC） has also been considered for demand management during congestion. Multi-line congestion cases have been considered to study the impact on demand response without and with FACTS devices. The proposed approach has been tested on the IEEE 24 bus test system.

Simultaneous Employment of Generation Rescheduling and Incentive-based Demand Response Programs for Congestion Management in Case of Contingency

Relieving congestion significantly influences the operation and security of the transmission network.Consequently,the congestion alleviation of transmission network in all power systems is imperative.Moreover,it could prevent price spikes and/or involuntary load shedding and impose high expenses on the transimission network,especially in case of contingency.Traditionally,the increasing or decreasing generation rescheduling has been used as one of the most imperative approaches for correctional congestion management when a contingency occurs.However,demand response programs(DRPs)could also be a vital tool for managing the congestion.Therefore,the simultaneous employment of generation rescheduling and DRPs is proposed for congestion management in case of contingency.The objective is to reschedule the generation of power plants and to employ DRPs in such a way so as to lessen the cost of congestion.The crow search algorithm is employed to determine the solution.The accuracy and efficiency of the proposed approach are assessed through the tests conducted on IEEE 30-bus and 57-bus test systems.The results of various case studies indicate the better performance of the proposed approach in comparison with different approaches presented in the literature.

Review of Methods to Calculate Congestion Cost Allocation in Deregulated Electricity Market

With maturing deregulated environment for electricity market, cost of transmission congestion becomes a major issue for power system operation. Uniform Marginal Price and Locational Marginal Price (LMP) are the two practical pricing schemes on energy pricing and congestion cost allocation, which are based on different mechanisms. In this paper, these two pricing schemes are introduced in detail respectively. Also, the modified IEEE-14-bus system is used as a test system to calculate the allocated congestion cost by using these two pricing schemes.

The Phenomenal Alleviation of Transmission Congestion by Optimally Placed Multiple Distributed Generators Using PSO

In the current electricity paradigm, the rapid elevation of demands in industrial sector and the process of restructuring are the main causes for the overuse of transmission systems. Hence, the evolution of novel technology is the ultimate need to avoid the damages in the available transmission systems. An appreciable volume of renewable energy sources is used to produce electric power, after the implementation of deregulation in power system. Even though, they are intended to improve the reliability of power system, the unpredictable outages of generators or transmission lines, an impulsive increase in demand and the sudden failures of vital equipment cause transmission congestion in one or some transmission lines. Generation rescheduling and load shedding can be used to alleviate congestion, but some cases require quite few improved methods. With the extensive application of Distributed Generation (DG), congestion management is also performed by the optimal placement of DGs. Therefore, this research employs a Line Flow Sensitivity Factor (LFSF) and Particle Swarm Optimization (PSO) for the determination of optimal location and size of multiple DG units, respectively. This proposed problem is formulated to minimize the total system losses and real power flow performance index. This approach is experimented in modified IEEE-30 bus test system. The results of N-1 contingency analysis with DG units prove the competence of this proposed approach, since the total numbers of congested lines get reduced from 15 to 2. Hence, the results show that the proposed approach is robust and simple in alleviating transmission congestion by the optimal placement and sizing of multiple DG units.

Indices of Congested Areas and Contributions of Customers to Congestions in Radial Distribution Networks

Congestions are becoming a significant issue with an increasing number of occurrences in distribution networks due to the growing penetration of distributed generation and the expected development of electric mobility.Fair congestion management(CM)policies and prices require proper indices of congested areas and contributions of customer to congestions.This paper presents spatial and temporal indices for rapidly recognizing the seriousness of congestions from the perspectives of both magnitude violation and duration to prioritize the affected areas where CM procedures should be primarily activated.Besides,indices are presented which describe the contributions of customers to the congestions.Simulation tests on IEEE 123-bus and Australian 23-bus low-voltage distribution test feeders illustrate the calculation and capabilities of the proposed indices in balanced and unbalanced systems.

Exploring the impact of a coordinated variable speed limit control on congestion distribution in freeway

Over the past few decades, urban freeway congestion has been highly recognized as a serious and worsening traffic problem in the world. To relieve freeway congestion, several active traffic and demand management （ATDM） methods have been developed. Among them, variable speed limit （VSL） aims at regulating freeway mainline flow upstream to meet existing capacity and to harmonize vehicle speed. However, congestion may still be inevitable even with VSL implemented due to extremely high demand in actual practice. This study modified an existing VSL strategy by adding a new local constraint to suggest an achievable speed limit during the control period. As a queue is a product of the congestion phenomenon in freeway, the incentives of a queue build-up in the applied coordinated VSL control situation were analyzed. Considering a congestion occurrence （a queue build-up） characterized by a sudden and sharp speed drop, speed contours were utilized to demonstrate the congestion distribution over a whole freeway network in various sce- narios. Finally, congestion distributions found in both VSL control and non-VS control situations for various scenarios were investigated to explore the impact of the applied coordinated VSL control on the congestion distribution. An authentic stretch of V^hitemud Drive （I~~ID）, an urban freeway corridor in Edmonton, Alberta, Canada, was employed to implement this modified coordinated VSL control strategy; and a calibrated micro-simu- lation VISSIM model （model functions） was applied as the substitute of the real-world traffic system to test the above mentioned performance. The exploration task in this study can lay the groundwork for future research on how to improve the presented VSL control strategy for achieving the congestion mitigation effect on freeway.

Distribution Locational Marginal Pricing Based Equilibrium Optimization Strategy for Data Center Park with Spatial-temporal Demand-side Resources

This paper proposes a distribution locational marginal pricing(DLMP) based bi-level Stackelberg game framework between the internet service company(ISC) and distribution system operator(DSO) in the data center park. To minimize electricity costs, the ISC at the upper level dispatches the interactive workloads(IWs) across different data center buildings spatially and schedules the battery energy storage system temporally in response to DLMP. Photovoltaic generation and static var generation provide extra active and reactive power. At the lower level, DSO calculates the DLMP by minimizing the total electricity cost under the two-part tariff policy and ensures that the distribution network is uncongested and bus voltage is within the limit. The equilibrium solution is obtained by converting the bi-level optimization into a single-level mixed-integer second-order cone programming optimization using the strong duality theorem and the binary expansion method. Case studies verify that the proposed method benefits both the DSO and ISC while preserving the privacy of the ISC. By taking into account the uncertainties in IWs and photovoltaic generation, the flexibility of distribution networks is enhanced, which further facilitates the accommodation of more demand-side resources.

Transmission Use of System Charging for Differentiating Long-term Impacts from Various Generation Technologies

This paper proposes a novel transmission use of system(TUoS)charging method,which is able to 1)acknowledge the trade-offs between short-run congestion cost and long-run investment cost when justifying economic network investment,2)identify the impacts of different generation technologies on congestion cost and network investment,and 3)translate these impacts into economically efficient TUoS tariffs that differentiate generation technologies.An incremental capacity change from a generator will impact the congestion costs at each branch,which is then translated into the impacts on investment time horizons.The difference in the present values with and without the incremental change for a branch is its long-run incremental cost(LRIC).The final TUoS tariff for this generator is the sum of all LRIC triggered by its capacity increment.The proposed method is demonstrated on a modified IEEE 14-bus system to show its effectiveness over the traditional approach.Results show that it can provide cost-reflective TUoS tariffs for different generation technologies at the same sites by examining their respective impacts on congestion and investment.It thus can incentivize appropriate generation expansion to reduce congestion costs and ultimately network investment cost.

Approximating multi-purpose AC Optimal Power Flow with reinforcement trained Artificial Neural Network

Solving AC-Optimal Power Flow(OPF)problems is an essential task for grid operators to keep the power system safe for the use cases such as minimization of total generation cost or minimization of infeed curtailment from renewable DERs(Distributed Energy Resource).Mathematical solvers are often able to solve the AC-OPF problem but need significant computation time.Artificial neural networks(ANN)have a good application in function approximation with outstanding computational performance.In this paper,we employ ANN to approximate the solution of AC-OPF for multiple purposes.The novelty of our work is a new training method based on the reinforcement learning concept.A high-performance batched power flow solver is used as the physical environment for training,which evaluates an augmented loss function and the numerical action gradient.The augmented loss function consists of the objective term for each use case and the penalty term for constraints violation.This training method enables training without a reference OPF and the integration of discrete decision variable such as discrete transformer tap changer position in the constrained optimization.To improve the optimality of the approximation,we further combine the reinforcement training approach with supervised training labeled by reference OPF.Various benchmark results show the high approximation quality of our proposed approach while achieving high computational efficiency on multiple use cases.

Travel time reliability with and without the dynamic use of hard shoulder:Field assessment from a French motorway

Traffic management aims to ensure a high quality of service for most users by decreasing congestion and increasing safety. However, uncertainty regarding travel time decreases the quality of service and leads end-users to change their routes and schedules even when the average travel time is low, Indicators describing travel time reliability are being developed and should be used in the future both for the optimization and the assessment of active traffic management operation. This paper describes a managed lane experience on a motorway weaving section in France - hard shoulder running operation in rush hours. The paper is focused on travel time reliability indicators and their use for reliability assessment. It provides some discussions about the advantages and drawbacks of reliability indicators under different traffic conditions. It particularly shows the difference between using buffer times and buffer indexes. The paper also discusses the difficulty of interpreting the skew of travel time distribution for travel reliability,