Real-time Congestion Management Research Articles

Dynamic Incentive Pricing on Charging Stations for Real-Time Congestion Management in Distribution Network: An Adaptive Model-Based Safe Deep Reinforcement Learning Method

Dynamic Incentive Pricing on Charging Stations for Real-Time Congestion Management in Distribution Network: An Adaptive Model-Based Safe Deep Reinforcement Learning Method

Robust [formula omitted] control for line power flow tracking in power systems

Traditionally, the problem of congestion arising in power system networks due to various contingencies and varying renewable generation is addressed by optimal power flow-based methods, once in every 5–30 min. As these techniques are performed for a block period of time using forecasted data, they are not effective in real-time congestion mitigation through corrective actions. This paper proposes a controller-based approach for real-time congestion management through line power flow regulation. We consider a robust H∞ power flow tracking control strategy to regulate the line power flows to the specified limit, while attenuating the effect of disturbances on the system. Using synchrophasor measurements from the lines, the proposed controller can detect line overloading and automatically re-dispatch the selected generators and battery energy storage systems to maintain power flow within the desired limit. The robust line power flow tracking problem with disturbance rejection is formulated as a dynamic game between the controller and disturbance entities, and robust static output feedback H∞ controller gains are obtained via an iterative algorithm. The efficacy of the proposed controller in regulating line power flow and mitigating congestion is verified by extensive simulations on a 16-machine 68-bus test system and Kundur’s 4-machine 2-area test system. It is shown that the designed controller can regulate line power flows within 40–60 s, which is fast enough to avoid overheating of transmission lines. Furthermore, the proposed strategy is simple from the perspective of design and implementation, and outperforms the existing congestion management techniques.

Real-time congestion management for networked microgrids using optimal resources rescheduling and reconfiguration considering multi-level thermal rate

Real-time congestion management for networked microgrids using optimal resources rescheduling and reconfiguration considering multi-level thermal rate

Market-based Real Time Congestion Management in a Smart Grid Considering Reconfiguration and Switching Cost

Network Real-Time Congestion (RTC) is a bottleneck that limits energy transfer from the generation units or up-grid to the loads. Some factors, such as intermittent generation of renewable resources and forced outages of generating units and load forecasting errors, can lead to Real-Time Congestion Management (RTCM) in a smart grid network. RTCM is a set of methods to eliminate congestion in real-time. To implement RTCM, some approaches can be employed, including network reconfiguration by Remote Control Switches (RCS), load shedding generation and up-grid power rescheduling. In this paper, a two-stage programming model is proposed to find the optimal solution for RTCM using the integration of reconfiguration and market-based approaches. Therefore, following the occurrence of congestion, at the first stage, microgrid central controller (MGCC) or central energy manager implements reconfiguration as the lowest-cost approach to mitigating RTC. The Soccer League (SL) algorithm is employed at the first stage to find the optimal network topology. Subsequently, based on the results obtained from the first stage, a programming model is applied at the second stage to completely eliminate the RTC. The proposed model minimizes a weighted objective function that includes the generation and up-grid rescheduling cost, load shedding cost, switching cost, and congestion clearing time. In order to model switching costs, a new index is defined to prevent risky switching and the depreciation caused by frequent switching. This index is determined based on the critical locations in the network and the age of RCSs. The numerical results demonstrate the efficacy of the proposed model.

Optimal Distributed Frequency and Voltage Control for Zonal Electricity Markets

Zonal pricing is a well-suited mechanism to incentivize grid-supporting behavior of profit-maximizing producers and consumers operating on a large-scale power system. In zonal electricity markets, local system operators create individual price zones, which provide appropriate price signals depending on local grid conditions such as an excess or shortage of electrical energy in certain regions. In this paper, a real-time zonal pricing controller for AC power networks is presented that ensures frequency and voltage stability as well as Pareto efficiency of the resulting closed-loop equilibria. Based on a dynamic network model that takes line losses and power exchange with adjacent price zones into account, distributed continuous-time control laws are derived which require only neighbor-to-neighbor communication. Application to a real-time congestion management strategy illustrates how spatially and temporally differentiated prices enable grid-supportive operation of the participants without interventions by a superordinate control authority. Effectiveness of different zonal pricing concepts compared to an isolated grid operation is demonstrated through simulations on the IEEE-57 bus system.

Real Time Congestion Management Using Plug in Electric Vehicles (PEV’s): A Game Theoretic Approach

This paper presents a game-theoretic approach for solving network congestion in the Smart Grid. A smart grid environment with high penetration of Electric Vehicles (EVs) for a bidirectional power trade provides an opportunity to use the EVs for congestion management. Optimal power injected or drawn by the EV charging stations can be obtained to mitigate the congestion problem by solving a constrained optimization problem to minimize the change in the power injected at every bus while satisfying several constraints. EV charging stations will be requested to inject or draw optimal power as obtained from the optimization problem. This paper proposes a game-theoretic approach (Greed Game) to develop a smart pricing model and design a mechanism to remove congestion in the network. A unique Nash equilibrium is achieved in the game, which optimally fulfills the line power congestion management problem and yields maximum benefit for the EVs. The algorithm was applied to 16-machine 68-bus test systems, and it has been found that the proposed mechanism maximizes the gain of the EVs and solves the congestion problem in the Grid.

Modular SSSC for the future power system in the SEERC region

Recently, the world has seen disruptive innovation and change regarding the ways that energy is produced and consumed. The power industry is facing systemic challenges in planning and operation of the whole system. Particularly, high penetration levels of Renewable Energy Sources (RES) bring additional design considerations due to their variable nature and their limitations on reactive power or fault level contribution.Modular Static Synchronous Series Compensator (M-SSSC) technology enables dynamic power flow control and real-time congestion management. Its benefits have been proven in several regional deployments stretching globally from the US to Europe. Given the forecast RES uptake and the geographical intricacies of the Southeast European Regional Council (SEERC) region, the M-SSSC technology can support the grid development and operational strategies in both the short-term and the long-term.

An integrated approach for failure mitigation & localization in power systems

The transmission grid is often comprised of several control areas that are connected by multiple tie lines in a mesh structure for reliability. It is also well-known that line failures can propagate non-locally and redundancy can exacerbate cascading. In this paper, we propose an integrated approach to grid reliability that (i) judiciously switches off a small number of tie lines so that the control areas are connected in a tree structure; and (ii) leverages a unified frequency control paradigm to provide congestion management in real time. Even though the proposed topology reduces redundancy, the integration of tree structure at regional level and real-time congestion management can provide stronger guarantees on failure localization and mitigation. We illustrate our approach on the IEEE 39-bus network and evaluate its performance on the IEEE 118-bus, 179-bus, 200-bus and 240-bus networks with various network congestion conditions. Simulations show that, compared with the traditional approach, our approach not only prevents load shedding in more failure scenarios, but also incurs smaller amounts of load loss in scenarios where load shedding is inevitable. Moreover, generators under our approach adjust their operations more actively and efficiently in a local manner.

Two-tier demand response with flexible demand swap and transactive control for real-time congestion management in distribution networks

With increasing deployment of distributed energy resources, network congestion is becoming a major concern of distribution system operators for secure operation of distribution networks. Real-time congestion management is important as congestion may occur at real-time operation due to forecast errors and components failures. To alleviate real-time congestion within distribution networks, demand response based congestion management schemes have been proposed. However, customers’ requirements on energy rebound, i.e., periods and amounts of energy rebound, are not considered in the existing demand response based congestion management schemes. Moreover, customers’ willingness to provide flexibilities for congestion management is not taken into account either. To resolve these issues, this paper proposes a two-tier demand response scheme with flexible demand swap and transactive control for real-time congestion management in distribution networks. With flexible demand swap and swap market, real-time congestion can be efficiently solved. Customers are able to rebound energy in multiple periods and determine the amounts of energy rebound by themselves. The proposed transactive control based interactive mechanism between aggregators and customers considers customers’ willingness to provide flexibilities. The Roy Billinton Test System (RBTS) was used to conduct case studies to validate the proposed two-tier demand response scheme for real-time congestion in distribution networks. The case study results demonstrate that the proposed two-tier demand response scheme can resolve congestion efficiently while considering customers’ requirements on energy rebound and willingness to provide flexibilities for congestion management.

Real-Time Congestion Management in Distribution Networks by Flexible Demand Swap

In addition to the day-ahead congestion management in distribution networks, the real-time congestion management is very important because many unforeseen events can occur at the real operation time, e.g., loss of generation of distributed energy resources or inaccurate forecast of energy consumption or production. Flexibility service from demand will be a good option to solve the real-time congestions if the cost of activating the flexibility service is fully addressed. This paper proposes a new method, namely “swap,” to employ the flexibility service from electric vehicles and heat pumps for real time congestion management. The swap method can maintain the power balance of the system and avoid the imbalance cost of activating the flexibility service. An algorithm for forming swaps through optimal power flow and mixed integer linear programming is proposed to implement the swap method. Case studies were carried out to validate the efficacy of the proposed swap method for real time congestion management and the proposed algorithm for forming swaps. The settlement process for the swaps in different markets is analyzed.

Adaptive real-time congestion management in smart power systems using a real-time hybrid optimization algorithm

In deregulated power systems, where a short period of service interruption causes extreme financial and social damages to customers and service providers, it is necessary to develop optimal intelligent algorithms in order to minimize unforeseen service interruptions due to unavoidable real-time contingencies. Nowadays, regarding the high implementation of communication infrastructure in smart power systems, as well as accurate sensors for a variety of purposes, it is possible to effectively collect and analyze real-time and synchronized data, run fast intelligent algorithms and send control commands to controllers. This paper proposes an adaptive real-time congestion management (RTCM) method which optimally employs adaptive thermal ratings of transmission lines to manage real-time congestions using all power system capabilities. This algorithm is considered as an essential ancillary service in a power market, where all generation companies and customers can participate. In this algorithm, a demand response program is modeled and also a real-time hybrid optimization algorithm is developed to solve the RTCM problem aimed at finding the optimal solution during a short time span. Incorporating an adaptive artificial neural network along with a modified particle swarm optimization (PSO) algorithm is proposed in this paper as a real-time hybrid optimization method. Advantages and effectiveness of this method are demonstrated by numerical results from analyzing the modified 39-bus New England system.

Demand response for real-time congestion management incorporating dynamic thermal overloading cost

Capacity challenges are emerging in the low-voltage (LV) distribution networks due to the rapid proliferation of distributed energy resources (DERs) and increasing electrification of loads. The traditional approach of network reinforcement does not achieve the optimal solution due to the inherent uncertainties associated with the DERs. In this article, a methodology of real-time congestion management of MV/LV transformers is proposed. A detailed thermal model of the transformer is used in order to obtain the costs incurred by overloading. An agent-based scalable architecture is adopted to combine distributed with computational intelligence for the optimum procurement of flexibility. The efficiency of the proposed mechanism is investigated through network simulations for a representative Dutch LV network. Simulation results indicate that the methods can effectively alleviate network congestions, while maintaining the desired comfort levels of the prosumers.

Real Time Congestion Management in Power Systems Considering Quasi-Dynamic Thermal Rating and Congestion Clearing Time

In a smart power system, smart measurement infrastructure brings so many advantages in monitoring and control of the power system. This paper proposes an optimal real time transmission congestion management algorithm in a competitive electricity market based on real time measurements in a smart power system. Instead of static and fix thermal rates, quasi-dynamic thermal rates of transmission lines are calculated and used to solve the real time congestion management optimization problem. The optimization problem includes an optimal generation rescheduling and load shedding scheme to minimize the congestion management costs in a specific time interval. This time interval is introduced and defined as congestion clearing time in this paper. Furthermore, in the proposed algorithm, the congestion clearing time is divided into few subsequent subintervals aimed at considering loads and power system variations during rescheduling procedure. The proposed method could be used by system operators (SOs) to adapt the real time congestion management procedure with regard to the power system loads and generations, or system configuration variations during rescheduling and load shedding process. Numerical results obtained from running the algorithm on the modified IEEE 30-bus test system and the transmission network of Esfahan Regional Power Network (ERPN), as a real size power system, demonstrate convincingly the ability of the proposed algorithm in the congestion management cost and load shedding reduction, while it increases the power system security margin in intensive congestion cases significantly.

Comparison of Sen Transformer and UPFC for congestion management in hybrid electricity markets

Congestion management (CM) is one of the most important and challenging tasks of the Independent System Operator (ISO) in the deregulated environment. Recently, Sen Transformer (ST) has emerged as an important power flow control device which has the capability of power flow control over a wide range like UPFC. This device with a conventional transformer and tap changers has the capability of bidirectional control of active and reactive power and can play a very important role in future markets for mitigating the congestion problems. In this paper, the capability of ST has been utilized to manage transmission line congestion for hybrid based electricity market model. The main contribution of the paper is: (i) to develop an optimal rescheduling of generators strategy for real time congestion management and impact of ST for congestion management, (ii) the comparison of ST with unified power flow controller (UPFC) for congestion management, (iii) the secure bilateral transactions determination in a hybrid market model and congestion management with both power flow controllers in combined pool and bilateral market model. The optimal location of ST and UPFC has been obtained solving mixed integer non-linear programming model of congestion management. The proposed model has been applied for results on IEEE 24-bus RTS test system.

Line overload alleviation through corrective control in presence of wind energy

With the growth of wind energy for power generation, several transmission system operators (TSOs) have increasing difficulties to forecast congestions due to the unpredictable nature of the energy source. This paper proposes to enhance congestion management using a real time supervisor. This supervisor performs automatic and dynamic re-dispatchings using both variable speed wind generators and conventional generators. In order to minimize production constraints, the real time congestion management system is based on an indicator of the efficiency of a re-dispatching on the power flowing in an overloaded line. This approach leads to reduced re-dispatching costs and increased network reliability. The simulation of the IEEE 14-bus test power system enhanced by the supervision system is done using the software “EUROSTAG”. Grid integration of renewable generation is therefore increased through renewable production maximization while ensuring network security.

Cost Control of the Transmission Congestion Management in Electricity Systems Based on Ant Colony Algorithm

This paper investigates the cost control problem of congestion management model in the real-time power systems. An improved optimal congestion cost model is built by introducing the congestion factor in dealing with the cases: opening the generator side and load side simultaneously. The problem of real-time congestion management is transformed to a nonlinear programming problem. While the transmission congestion is maximum, the adjustment cost is minimum based on the ant colony algorithm, and the global optimal solu-tion is obtained. Simulation results show that the improved optimal model can obviously reduce the adjust-ment cost and the designed algorithm is safe and easy to implement.

Advanced real-time congestion management through both pool balancing market and bilateral market

A framework for real-time congestion management under a marker structure similar to the newly proposed UK trading arrangement is presented in this letter, in which not only resources in balancing market but also some bilateral contracts can be dispatched if necessary. The linearized model of a modified optimal power flow (OPF) is proposed to implement such a framework. A 5-bus test system is used to illustrate the effectiveness of the proposed method.

A Knowledge-Based System for Integrated Freeway and Arterial Incident Management and Control: Implementation and Validation Results

Abstract This paper describes the process and the results or the evaluation of Traffic Congestion Manager (TCM), a knowledge-based system for Traffic Management Center operator assistance in real-time congestion management The process of evaluation included the validation of the correctness of the underlying knowledge base and the assessment of the system’s performance, through the definition of several simulation-based scenarios. The results demonstrate the ability of the system to effectively reduce driver travel time and stop time in the network under congested conditions, through the implementation of real-time traveler information and signal control. The results also provide insights into some of the weaknesses of the TCM approach, that will be addressed to fine-tune its reasoning capabilities and improve its performance.