Load Pickup Research Articles

Tabu Search- and Conservation Voltage Reduction-Based Service Restoration Algorithm for Cold Load Pickup Conditions

Cold load pickup (CLPU) has become a major concern for distribution systems as the use of thermostatically controlled devices continues to increase. CLPU reduces load diversity, which thus increases the total load on the system. Without a sufficient supply, the system must be divided into sections and sequentially restored to prevent system overload, which can lead to a second blackout. This paper presents a service restoration strategy that optimizes the sequence of section restoration to minimize the total restoration time without additional generation in the system. The search for the optimum combination of restored sections is performed by implementing Tabu Search (TS). The CLPU effects are mitigated by using conservation voltage reduction (CVR). The power consumption of the load will decrease, which allow more load sections to be connected without overloading the system. To verify the algorithm, two simulations were performed. The first simulation compares the performance of the proposed algorithm to that of the greedy algorithm. The second simulation demonstrates the superior ability of the proposed algorithm to maintain an acceptable operating voltage during the restoration process.

Cold load pickup model parameters based on measurements in distribution systems

The electric power demand after an outage can be significantly different from normal values. A major cause of this is the loss of statistic diversity in load devices that interact with some kind of storage. Examples are heating and cooling appliances as well as some applications of pumps and devices with integrated batteries. This phenomenon is known as cold load pickup (CLPU). Changes in load after an outage are of high relevance for investigations of power system restoration, especially active power control and balancing of generation and load. Besides the very fast inrush effects relevant for fast acting protection and short-term stability, long-term dynamics, thermal overload of equipment and active power balancing in the presence of uncontrolled distributed generation consider a time frame of minutes to hours. To predict CLPU from physical models, a lot of knowledge is required. Since this information is rarely available in the real-world applications, the authors investigate typical ranges of CLPU behaviour. The authors analyse measurement data from reconnections after real outages of medium voltage feeders in Germany and provide parameters for the exponential decay model of CLPU. The relevance of the application of the presented model for long-term stability studies is demonstrated by an example.

A Data-Driven Framework for Assessing Cold Load Pick-Up Demand in Service Restoration

Cold load pick-up (CLPU) has been a critical concern to utilities. Researchers and industry practitioners have underlined the impact of CLPU on distribution system design and service restoration. The recent large-scale deployment of smart meters has provided the industry with a huge amount of data that is highly granular, both temporally and spatially. In this paper, a data-driven framework is proposed for assessing CLPU demand of residential customers using smart meter data. The proposed framework consists of two interconnected layers: 1) At the feeder level, a nonlinear auto-regression model is applied to estimate the diversified demand during the system restoration and calculate the CLPU demand ratio. 2) At the customer level, Gaussian Mixture Models (GMM) and probabilistic reasoning are used to quantify the CLPU demand increase. The proposed methodology has been verified using real smart meter data and outage cases.

Real-Time Optimized Load Recovery Considering Frequency Constraints

A self-healing power grid can mitigate the impact of power outages and recover the affected area swiftly. To properly size the optimal amount and location of de-energized loads in the presence of both conventional and renewable sources, a precise optimization-based load recovery tool is proposed in this paper. The steady-state and transient behaviors of loads have been taken into account. The proposed tool contains three separate problems that interact with each other during the restoration period. The first-level problem determines the dispatching of generation units. The second-level problem comprises the non-linear algebraic equations, whose task is to provide the initial conditions for the third-level problem. Finally, the third-level problem intends to determine the optimal location and amount of load pickup through modeling the governor of generators as well as loads’ dynamic behaviors. To examine the effectiveness of the proposed tool, a modified IEEE 39-system under a partial blackout condition has been tested. The results are verified by a commercial time-domain simulation software (PSS/E) to demonstrate the accuracy of the proposed approach. Ultimately, the impact of wind turbine frequency control on the load recovery process has been studied under different penetration levels.

Automatic restoration of large-scale distribution networks with distributed generators, voltage control devices and heating loads

Energy supply interruption by permanent faults causes economic and social problems, dissatisfaction of customers and penalization to electrical power distribution companies. In the procedure to restore distribution networks, power interruption damages must be minimized by energizing the largest possible number of customer loads in the shortest time interval. The restoration problem of distribution networks can be mathematically formulated as a mixed integer nonlinear programming problem that is non-convex of type NP-complete. The existence of heating loads causes the cold load pickup condition in the distribution network that requires the step-by-step procedure increasing the complexity of the restoration problem. In this work, therefore, it is proposed a methodology to solve the restoration problem of large-scale distribution networks with heating loads where the mathematical model is subjected to intentional islanding of distributed generation and voltage control through optimized adjustments on capacitor banks and voltage regulators. The outcomes are achieved under a testing distribution system with 53 nodes and a real system with 7052 nodes.

Optimal Skeleton-Network Restoration Considering Generator Start-Up Sequence and Load Pickup

Power system restoration comprises of three stages, i.e., the preparation stage, system restoration stage, and load restoration stage. This paper addresses the first two stages by a new skeleton-network restoration strategy considering the generator start-up sequence (GSUS) and load pickup. The proposed restoration strategy consists of three mathematical models: 1) GSUS model; 2) transmission line restoration (TLR) model; and 3) load pickup (LDP) model. The GSUS model aims to maximize the overall system generated energy considering the actual power system topology. The TLR model is to attain an optimal skeleton-network by identifying the restoration sequence of transmission lines. Critical loads are restored to keep the voltage within a specified respective threshold, and the load amounts are optimized in the LDP model. The proposed GSUS model and TLR model are both solved by the branch-and-bound method, while the LDR model is solved by the interior point method as well as the branch-and-cut method. Finally, the performance of the proposed method is demonstrated by two case studies on the New England 10-unit 39-bus system and an actual power system in Guangdong, China, respectively.

Islanding strategy for restoring electric power supply by means of electric vehicle idle power against cold load pickup

This study presents a new method of forming emergency-responsive autonomous islands for restoring electric power supply using an electric vehicle (EV) idle power against the so-called cold load pickup (CLPU). Two stepwise discretisation methodologies are firstly proposed, which are used to discretise arbitrarily given cold-load models and aggregate discretised EV discharge models, respectively. Then, under the framework of mixed-integer linear programming, a methodology of forming particular responsive restoration islands is built, which aims to maximise the recovered energy of interrupted end-users given discretised CLPU and EV discharge models. Comparative analyses via the proposed method against existing algorithms are conducted on both a partial IEEE RBTS-BUS6 test feeder and a practical feeder. Specifically, the conducted analyses investigate the impacts of the CLPU on energy restoration and the assistance performance of discharge power from EV's on boosting the capability of responsive restoration islands to restore the interruption. Moreover, an especial relationship is studied, between the utilisation rate of total discharge power of an EV parking lot and the rate of restored energy concerning the total lost energy.

Optimal distribution system restoration using PHEVs

Power outages cost billions of dollars every year and jeopardise the lives of hospital patients. Traditionally, power distribution system takes a long time to recover after a major blackout, due to its top-down operation strategy. New technologies in modern distribution systems bring opportunities and challenges to distribution system restoration. As fast response energy resources, plug-in hybrid electric vehicles (PHEVs) can accelerate the load pickup by compensating the imbalance between available generation and distribution system load. This study provides a bottom-up restoration strategy to use PHEVs for reliable load pickup and faster restoration process. The optimisation problem of finding load pickup sequence to maximise restored energy is formulated as a mixed integer linear programming (MILP) problem. Moreover, the coordination between transmission and distribution restoration is developed to efficiently restore the entire system back to normal operating conditions. Simulation results on one 100-feeder test system demonstrate the efficiency of MILP-based restoration strategy and the benefit from PHEVs to restore more energy in given restoration time. The proposed restoration strategy has great potential to facilitate system operators to achieve efficient system restoration plans. It also provides incentives to deploy a large amount of PHEVs to improve system resiliency.

Оценка технической возможности и целесообразности использования паровой турбины ПГУ-450 для регулирования частоты в энергосистеме

A binary combined cycle plant (CCP) with the twin power unit configuration includes two active load control components: a gas turbine and a steam turbine. At present, CCPs are actively involved in frequency control in the power system. High load pickup and decreasing rates in combination with a low inertia impart high maneuverability indicators to the gas turbine, due to which it plays the leading role in the CCP power output control system. On the other hand, owing to a low dependence of the steam turbine efficiency on the current load and its high maneuverability due to using the storage capacity of the heat-recovery steam generator drums, the steam turbine can also be used in controlling the power system frequency. The article presents the results from modeling the PGU-450 power unit participation processes in controlling the power system frequency on the power unit’s computerized training simulator model with active involvement of the CCP steam turbine in adjustment of the CCP power output in the initial (most complex) load variation stage in accordance with the power system requirements through using the heat stored in the drums of heat- recovery steam generators. As a result of the accomplished studies, the technical feasibility and advisability of the proposed technology are shown. The technical advisability of actively involving the CCP steam turbine in control of frequency by using feed-forward control of the steam turbine is stemming from its high maneuverability due to a significant storage capacity of the steam generator drums, and the economic feasibility is due to the fact that both the steam turbine and gas turbine efficiencies change only slightly in changing the load within the required CCP power output variation limits.

Frequency Regulation by Electric Vehicle during Grid Restoration using Adaptive Optimal Control

Abstract The restoration process of power system after a blackout involves generation restart, energization of buses/power lines and sequential load pickup. In the process of doing so, system frequency tends to deviate from its nominal value and should be checked within the tolerance range. Electric vehicles (EVs) with bidirectional power flow capabilities can act as fast response compensator during the generation-load imbalance while ongoing restoration process. The system dynamics is associated with system parameters which are often not known. To this end, an adaptive optimal controller has been formulated to provide the EV an optimal charging/discharging solution at the behest of no system information available at controller end. Simulation results confirm the regulation of system frequency by control action obtained through EV charging/discharging.

Modern network reconfiguration techniques for service restoration in distribution systems: A step to a smarter grid

This paper presents state of the art reconfiguration techniques used for service restoration in distribution systems under different practical considerations. The different formulations of the problem together with the different solution methods are presented to show the advantages and disadvantages of each formulation and each solution. Other aspects that enhance the solution practicability such as load variations, load priority, cold load pickup, network connectivity representation, and existence of distributed generation are considered with directions for future research to convert current distribution system into an automatic self-healing system, which is the main pillar of the smart grid. The control method and the communication techniques used in the execution of the reconfiguration problem solution are highlighted and the new research directions in each issue are emphasized. The conclusion about the reconfiguration problem the formulation, the solution method, and the guidelines about unexplored research areas in this topic are presented at the end of the paper.

Multi-Time Step Service Restoration for Advanced Distribution Systems and Microgrids

Modern power systems are facing increased risk of disasters that can cause extended outages. The presence of remote control switches, distributed generators (DGs), and energy storage systems (ESS) provides both challenges and opportunities for developing post-fault service restoration methodologies. Inter-temporal constraints of DGs, ESS, and loads under cold load pickup conditions impose extra complexity on problem formulation and solution. In this paper, a multi-time step service restoration methodology is proposed to optimally generate a sequence of control actions for controllable switches, ESSs, and dispatchable DGs to assist the system operator with decision making. The restoration sequence is determined to minimize the unserved customers by energizing the system step by step without violating operational constraints at each time step. The proposed methodology is formulated as a mixed-integer linear programming model and can adapt to various operation conditions. The proposed method is validated through several case studies that are performed on modified IEEE 13-node and IEEE 123-node test feeders.

Coordination of Wind Farm and Pumped-Storage Hydro for a Self-Healing Power Grid

The increasing penetration of wind energy poses great challenges to the operation of power systems in normal and emergency states. However, energy storage technologies can help accommodate wind power uncertainty and variability due to their flexible characteristics. This paper focuses on the restoration phase, and provides a novel coordination strategy of wind and pumped-storage hydro (PSH) units for a faster and reliable self-healing process. The wind-PSH assisted power system restoration is formulated as a two-stage adaptive robust optimization problem. The first-stage problem determines the start-up sequence of generators and the energization times of transmission paths; and the second-stage problem decides load pickup sequences, wind power dispatch levels, and PSH units’ operating modes. The column-and-constraint generation decomposition algorithm is applied to solve the two-stage adaptive robust optimization problem, which has a mixed-integer optimization in the inner-level problem. The developed coordination strategy is tested on the modified IEEE 39-bus system. Numerical results demonstrate that the coordinated wind and PSH units can increase the total energy served, enhance wind power dispatchability, and reduce wind power curtailment.

Second-order conic programming model for load restoration considering uncertainty of load increment based on information gap decision theory

Load restoration is an important issue for power system restoration after a blackout. A second order conic programming (SOCP) model is proposed based on the information gap decision theory (IGDT) to maximize load pickup considering the uncertainty of load increment. Because distribution functions of load increment are difficult to obtain, the optimization of load pickup is transformed to maximize the fluctuation range of load increment by the IGDT. The derived optimal fluctuation range can ensure that the reenergized system is secure, and the amount of load pickup is always better than the specified expectation. Moreover, because the optimization model of the fluctuation range is a mixed-integer nonlinear model which is challenging to solve accurately and efficiently, the nonlinear model is transformed into a SOCP model that can be efficiently solved using CPLEX. The efficiency of the IGDT-based SOCP model is validated using the New England (10-machine 39-bus) system. The simulation results show that the derived load pickup shows expected robustness with respect to the load increment uncertainty.

Distribution Power System Reliability Assessment Considering Cold Load Pickup Events

Power system reliability measures for residential distribution networks have gained a lot of attention in recent years. This is due to the introduction of smart grids, where the quality of the delivered power becomes a crucial issue. In this paper, a new methodology to include cold load pickup (CLPU) events in the reliability assessment of power distribution systems is proposed. The developed methodology presents a platform to include CLPU events in the reliability assessment assuming an optimal systematic procedure is used to restore the affected load points. The methodology is applied on a test system using a state-of-the-art search technique named lightning search algorithm (LSA). The optimal plan takes into account electrical code protection system settings, system operation limits, and stochastic behavior of the load. The reliability indices for a base case without considering CLPU is compared with the case that considers CLPU assuming an optimal restoration. Both cases are compared further under fixed-restoration plans with different time intervals. The results demonstrate the effectiveness of the proposed methodology to improve the distribution network reliability by considering the CLPU events.

An Optimal Source-Load Coordinated Restoration Method Considering Double Uncertainty

The security of power system restoration is severely affected by uncertain factors, especially the start-up time of generating unit and the amount of load pick-up. Solving the optimization restoration problem is challenging since it needs to determine different priorities in which units and loads are restored with the consideration of double uncertainty. Therefore, an optimal source-load coordinated restoration method that is based on information gap decision theory (IGDT) is proposed. Firstly, the time-domain restoration characteristics of black-start unit (BSU), non-black-start unit (NBSU), and load are described with analysis of double uncertainty. On this basis, a coupled multi-objective optimization model is built with double uncertainty, in which source-load coordinated restoration is realized. Then, IGDT is adopted to convert the uncertainty optimization model to a certain one with robustness, which tolerates the most uncertainty and still meets the desired requirement. Finally, the optimization model is solved by non-dominated genetic algorithm II (NSGA-II). The effectiveness and robustness of the proposed method is further illustrated through a case study based on the IEEE 39-bus system.

Synchrophasor‐enabled power grid restoration with DFIG‐based wind farms and VSC‐HVDC transmission system

An innovative system restoration strategy using doubly-fed induction generator-based wind farms is proposed. The strategy involves retention of charge in the DC bus following a blackout and `Hot-Swapping' between direct flux control mode and conventional grid-connected mode, which does not require resetting of any controller dynamic states and avoids the need for energy storage. An autonomous synchronisation mechanism enabled by remote synchrophasors is also proposed. A blacked-out system, which includes a wind farm and a voltage source converter (VSC)-HVDC connected to a network unaffected by blackout, is used as the study system. Transmission line charging and load pickup is performed using the wind farm in flux control mode while the VSC-HVDC system conducts the same process for another portion of the system. The proposed `Hot-Swapping' and autonomous synchronisation approach is applied to connect the two parts of the grid and switch the wind farm to grid connected mode of operation. The results are demonstrated in a hybrid co-simulation platform where the aforementioned system is modelled in EMT-type software and the rest of the network is represented in a phasor framework.

Modelling of Active Distribution Networks for Power System Restoration Studies

This paper presents an aggregated model of Active Distribution Networks (ADNs) appropriate for power system restoration studies. The ADN considers aggregated loads and inverter based generation, such as wind and photovoltaic generation. The load model incorporates: i) time series data for consumption; ii) voltage and frequency dependency; iii) under frequency load shedding; iv) disconnection behavior; and v) cold load pick-up. The generation model considers: i) time series data for generation; ii) over frequency active power reduction; iii) disconnection; and iv) reconnection behavior. The ADN model covers dynamic phenomena in the time scale of tens of seconds up to several minutes with the focus on frequency dynamics. Moreover, the model was tested in several case studies and shows adequate dynamic behavior.

Advanced power system partitioning method for fast and reliable restoration: toward a self‐healing power grid

The recovery of power system after a large area blackout is a critical task. To speed up the recovery process in a power grid with multiple black-start units, it would be beneficial to partition the system into several islands and initiate the parallel self-healing process independently. This study presents an effective network partitioning algorithm based on the mixed-integer programming technique and considering the restoration process within each island. The proposed approach incorporates several criteria such as self-healing time, network observability, load pickup capability, and voltage stability limits. It can quickly provide multiple partitioning schemes for system operators to choose based on different requirements. Experimental results are provided to demonstrate the effectiveness of the proposed approach for IEEE 39-bus and IEEE 118-bus standard test systems. Also, the sensitivity of the partitioning solution with respect to the various parameters is presented and discussed. Ultimately, the advantage of the proposed method is demonstrated through the comparison with other references.

A Hierarchical Response-Based Approach to the Load Restoration Problem

Power system resilience is the ability of an electric power grid to withstand, absorb, and rapidly recover from low-probability high-impact disasters. In recent years, several major incidents all around the world have highlighted the need for operative solutions in order to materialize the resilience of the power grid in response to such catastrophic events. With the advent of synchrophasor technology in modern power systems, it has emerged as a promising measure to improve the resilience of power network. At the event of widespread outages, synchrophasor technology could facilitate the restoration procedure, reduce total outage duration, and mitigate damaging impacts to critical loads. This paper proposes a two-stage hierarchical approach to the transmission-level load restoration problem based on synchrophasors. The first stage determines the optimal amount and location of load pickups, and in the second stage, the predetermined decisions are implemented and the network stability is monitored until reaching a viable equilibrium point. The whole problem is decomposed into a set of tractable subproblems in order to improve the computational efficiency. The effectiveness of the proposed approach is further illustrated through a case study based on the IEEE 39-bus system.