Load Curtailment Research Articles

Undisruptive load curtailment scheme to ensure voltage stability margin

Curtailable loads (CLs) are regarded as one of the last lines of defence to maintain the security and stability of power systems. Nevertheless, if organised carefully, they can also be exploited as preventive security procurement measures for power systems. This is especially true for heavily loaded power systems in which the normal control schemes are already exhausted. In this study, a new power market approach is proposed to utilise CLs’ potential in ensuring voltage stability margin (VSM) for heavily-loaded power systems. The proposed method aims to provide a mechanism to engage CLs as active players in the power market, and hence, minimise the disruptive effect of the load curtailment. Next, CLs participation in the power market is utilised to minimise the cost of achieving a desired level of VSM, at normal and N − 1 contingency conditions, in a preventive manner. The most efficient load curtailment and power generation pattern are determined, using a modified particle swarm optimisation algorithm, to minimise the system's total cost, while meeting grid's VSM requirements. The simulation results verify that the desired level of VSM can be effectively obtained using the proposed approach, with much less operational cost compared to the established methods.

Probabilistic load curtailment estimation using posterior probability model and twin support vector machine

Estimating the potential load curtailments as a result of hurricane is of great significance in improving the emergency response and recovery of power grid. This paper proposes a three-step sequential method in identifying such load curtailments prior to hurricane. In the first step, a twin support vector machine (TWSVM) model is trained on path/intensity information of previous hurricanes to enable a deterministic outage state assessment of the grid components in response to upcoming events. The TWSVM model is specifically used as it is suitable for handling imbalanced datasets. In the second step, a posterior probability sigmoid model is trained on the obtained results to convert the deterministic results into probabilistic outage states. These outage states enable the formation of probability-weighted contingency scenarios. Finally, the obtained component outages are integrated into a load curtailment estimation model to determine the expected potential load curtailments in the grid. The simulation results, tested on the standard IEEE 118-bus system and based on synthetic datasets, illustrate the high accuracy performance of the proposed method.

Optimal Coordination of Aggregated Hydro-Storage with Residential Demand Response in Highly Renewable Generation Power System: The Case Study of Finland

Current energy policy-driven targets have led to increasing deployment of renewable energy sources in electrical grids. However, due to the limited flexibility of current power systems, the rapidly growing number of installations of renewable energy systems has resulted in rising levels of generation curtailments. This paper probes the benefits of simultaneously coordinating aggregated hydro-reservoir storage with residential demand response (DR) for mitigating both load and generation curtailments in highly renewable generation power systems. DR services are provided by electric water heaters, thermal storages, electric vehicles, and heating, ventilation and air-conditioning (HVAC) loads. Accordingly, an optimization model is presented to minimize the mismatch between demand and supply in the Finnish power system. The model considers proportions of base-load generation comprising nuclear, and combined heat and power (CHP) plants (both CHP-city and CHP-industry), as well as future penetration scenarios of solar and wind power that are constructed, reflecting the present generation structure in Finland. The findings show that DR coordinated with hydropower is an efficient curtailment mitigation tool given the uncertainty in renewable generation. A comprehensive sensitivity analysis is also carried out to depict how higher penetration can reduce carbon emissions from electricity co-generation in the near future.

MPLP-Based Fast Power System Reliability Evaluation Using Transmission Line Status Dictionary

Composite power system reliability evaluation is computationally time-consuming because the optimal power flow (OPF) with the least load curtailment is calculated for a large number of samples. Most current studies of probabilistic simulation methods focus on sampling techniques to improve the sampling efficiency and decrease the calculations of the OPF. This paper proposes a fast reliability evaluation method that accelerates the calculation of the OPF using multi-parametric linear programming (MPLP). In this paper, the sampled transmission line statuses are considered by the transmission line status dictionary (TLSD). We match the line status of each sample with the scenarios in the TLSD. For matching samples, the generation status and the sampled load are treated as MPLP parameters of the DC-OPF evaluation model. A dynamic learning algorithm is applied to solve the MPLP problem. Case studies are conducted on both IEEE Reliability Test Systems and a provincial power system in China. The results show that the proposed method improves the evaluation efficiency by 23–30 times compared with the normal DC-OPF-based model.

Efficient Customer Selection Process for Various DR Objectives

For timely and effective dispatch of demand response (DR) events, utilities require an efficient customer selection process that considers multiple factors, such as customer energy consumption patterns, customer compliance, and DR event time intervals. Moreover, customer targeting strategies may be different depending on the priority of each DR program. Thus, this paper investigates how to efficiently select customers for certain types of incentive-based DR programs (e.g., direct load control and curtailable load control) with various objectives based on hourly energy consumption data. To cope with computation issues arising from big data, we propose a filtering process that reduces the number of eligible customers based on proper criteria and use a novel approximate algorithm to solve the proposed customer selection problems in the format of a stochastic knapsack problem. This paper also suggests how to consider a customer compliance factor in the customer selection process.

A data mining-driven incentive-based demand response scheme for a virtual power plant

Given the increasing prevalence of smart grids, the introduction of demand-side participation and distributed energy resources (DERs) has great potential for eliminating peak loads, if incorporated within a single framework such as a virtual power plant (VPP). In this paper, we develop a data mining-driven incentive-based demand response (DM-IDR) scheme to model electricity trading between a VPP and its participants, which induces load curtailment of consumers by offering them incentives and also makes maximum utilization of DERs. As different consumers exhibit different attitudes toward incentives, it is both essential and practical to provide flexible incentive rate strategies (IRSs) for consumers, thus respecting their unique requirements. To this end, our DM-IDR scheme first employs data mining techniques (e.g., clustering and classification) to divide consumers into different categories by their bid-offers. Next, from the perspective of VPP, the proposed scheme is formulated as an optimization problem to minimize VPP operation costs as well as guarantee consumer’s interests. The experimental results demonstrate that through offering different IRSs to categorized consumers, the DM-IDR scheme induces more load reductions; this mitigates critical load, further decreases VPP operation costs and improves consumer profits.

Optimal Scheduling of Demand Response in Pre-Emptive Markets Based on Stochastic Bilevel Programming Method

This paper proposes a new strategy for an independent system operator (ISO) to trade demand response (DR) with different DR aggregators while considering various operational constraints. The ISO determines the energy scheduling and reserve deployment in a pre-emptive market while setting DR contracts with the DR aggregators. The ISO applies a two-stage stochastic programming to cope with the uncertainty associated with wind power production. DR aggregators’ behavior is modeled through a profit maximization function. Aggregators determine their DR trading shares with ISO and customers through three DR options, including load curtailment, load shifting, and load recovery. A stochastic bilevel problem is formulated, in which in the upper level, the ISO minimizes the total operation cost, and in the lower level, the DR aggregator maximizes the profit. Afterwards, the problem is transferred to a single-level mathematical problem with equilibrium constraints by replacing the lower level program with its Karush–Kuhn–Tucker (KKT) conditions. As a result, the total operation cost is reduced using the proposed method comparatively to run the program without considering the lower level.

Impact Analysis of Survivability-Oriented Demand Response on Islanded Operation of Networked Microgrids with High Penetration of Renewables

Microgrids have the potential to withstand the power outages due to their ability of islanding and potential to sustain the penetration of renewables. Increased penetration of renewables can be beneficial but it may result in curtailment of renewables during peak generation intervals due to the limited availability of storage capacity while shedding loads during peak load intervals. This problem can be solved by adjusting the load profiles, i.e., demand response (DR) programs. In contrast to the existing studies, where DR is triggered by market price signals, a local resource-triggered survivability-oriented demand response program is proposed in this paper. The proposed DR program is triggered by renewable and load level of the microgrid with an objective to minimize the load shedding and curtailment of renewables. The uncertainties in load and renewables are realized via a robust optimization method and the worst-case scenario is considered. The performance of the proposed method is compared with two conventional operation cases, i.e., independent operation case and interconnected operation case without DR. In addition, the impact of renewable penetration level, amount of shiftable load, and load absorption capacity on the performance of the proposed method are also analyzed. Simulation results have proved the proposed method is capable of reducing load shedding, renewable curtailment, and operation cost of the network during emergencies.

Reserve Improvement by Load Curtailment Service for Gas-Electric Systems Integrated with Wind Power

A new model is proposed for the optimal scheduling of gas-electric integrated energy systems, considering the incentive demand response (IBDR) to provide the reserve ancillary service. By incorporating the IBDR, one hand, the system can give full play to the complementary functions of different energy sources to enhance the efficiency of the system; on the other hand, more flexibility can be utilized to cope with the uncertainty of wind power with IBDR providing reserves with conventional units simultaneously. We establish the IBDR model with segmental price-quantity curves, and the transient gas flow and steady state power flow are combined to formulate the operation model of gas-electric integrated systems. A 6-node power network and a 12-node natural gas network were selected to construct a gas-electric integrated energy system. The simulation results show that the IBDR can improve the flexibility of the system and decrease the total operation cost of the gas-electric integrated energy system.

Preventive and Corrective Control Actions on Power Systems via Heuristic Optimization Methods with Consecutive Search Space Reduction

This paper represents a new methodology to improve the performance of population-based optimization algorithms designed for corrective and preventive control actions enhancing power system dynamic security. Unlike many adaptive approaches employing the parameters such as population size, crossover or mutation rates, the proposed method for the performance improvement is based on the reduction of the search space size. In the method, optimization algorithms run consecutively, while the size of the search space is reduced according to the objective function values attained during the optimization process. In this study, generation rescheduling combined with load curtailment is applied as a preventive control action, whereas load shedding is selected as a corrective control. Each of these control actions is determined through the formulation of a security constrained optimization problem and its solution via population-based optimization algorithms. The proposed methodology is successfully applied to differential evolution, particle swarm optimization, artificial bee colony optimization, and big bang big crunch optimization methods for solving the optimization problems in a 16-generator-68-bus system and the Turkish Transmission System with 750 generators and 2600 buses. It is demonstrated that the proposed method provides better solutions with lesser computational complexity than the ones obtained by using fixed search space sizes.

Enhancing Resilience Level of Power Distribution Systems Using Proactive Operational Actions

Growing widespread outages in power systems caused by natural disasters have highlighted the necessity of applying defensive approaches with the goal of prompt service restoration. In this context, this paper proposes a stochastic model with the goal of optimally using proactive operational actions before the upcoming disturbance hits. The actions considered in this study include network reconfiguration and crew prepositioning. To take the effective actions, the model simulates probable damages caused by the events via a set of scenarios generated by Monte Carlo simulation method. The proposed model aims at minimizing the expected load curtailment caused by the event over the scenarios. To calculate the amount of load curtailments, potential post-disturbance actions are also considered in the model. The model is mathematically formulated in mixed integer linear programming (MILP) fashion which can be easily solved via available software packages. A standard distribution system with a realistic set of data is employed to validate the effectiveness of the proposed model.

Optimal Operation of Unbalanced Three-Phase Islanded Droop-Based Microgrids

This paper presents a new mixed-integer nonlinear programming (MINLP) model for the optimal operation of unbalanced three-phase droop-based microgrids. The proposed MINLP model can be seen as an extension of an optimal power flow for microgrids operating in islanded mode, that aims to minimize the total amount of unsupplied demand and the total distributed generator (DG) generation cost. Since the slack bus concept is not longer valid, the proposed model considers the frequency and voltage magnitude reference as variables. In this case, DGs units operate with droop control to balance the system and provide a frequency and voltage magnitude reference. Additionally, a set of efficient linearizations are introduced in order to approximate the original MINLP problem into a mixed-integer linear programming (MILP) model that can be solved using commercial solvers. The proposed model has been tested in a 25-bus unbalanced three-phase microgrid and a large 124-node grid, considering different operational and time-coupling constraints for the DGs and the battery systems (BSs). Load curtailment and different modes of operation for the wind turbines have also been tested. Finally, an error assessment between the original MINLP and the approximated MILP model has been conducted.

Dealing with Supply Disruptions on the European Natural Gas Market: Infrastructure Investments or Coordinated Policies?

Natural gas plays an important role in many European energy systems especially with regard to the envisioned transition towards a less carbon intensive energy supply. Being dependent on imports – especially from Russia – this raises questions about the future development and security of Europe’s gas supply. Using a numerical network model of the European gas system we assess 1) the potential impact of infrastructure extensions for Europe’s gas supply and 2) the role of supply security policies in coping with a disruption of Russian imports via the Ukraine. Our results indicate that overall the European gas infrastructure is sufficient for average market conditions. Due to the strong dependency on Russian imports, however, disruptions during the winter months could lead to load curtailment. Projected network extension (Southern Gas Corridor, Nord Stream 2, and new LNG terminals) or a strategic storage policy coordinated across Europe has the potential to reduce this shortage. The positive impact of an extended network, however, also depends on the capability of the global gas market to provide flexible gas that can be reallocated towards Europe. The majority of demand curtailment can already by countered by a relatively modest amount of strategic storage (20% to 30%) if their use during crisis situations is coordination across European countries.

Resilience-Oriented Optimal Operation of Networked Hybrid Microgrids

A resilience-oriented optimization strategy is proposed in this paper by considering feasible islanding in normal operation and survivability of critical loads during emergency period. A resilience index is proposed for assessing the capability of each microgrid to feed local critical loads during sudden power disruptions. Based on the values of the proposed resilience index, local optimization results are revised by changing the commitment status of dispatchable generators and energy storage elements. The uncertainties associated with renewable generations and loads are realized via robust optimization method. The adjustable power bounds, suggested by each microgrid for minimizing the operation cost of the network, are also revised through the proposed resilience index. In emergency mode, decision between feeding of lesser critical loads and battery charging along with a strategy for minimization of load curtailment during switching of scheduling windows is considered. These two considerations assure the survivability of critical loads during emergency period. Finally, an incremental cost consensus algorithm is used for optimal allocation of surplus power among the connected microgrids having unserved loads. Three different network topologies are considered in emergency mode for assessing the performance of the proposed approach.

Resilient Configuration Approach of Integrated Community Energy System Considering Integrated Demand Response Under Uncertainty

Integrated community energy system (ICES) enables multi-energy synergy and impulses interaction of integrated demand response (IDR) from the demand side. Nevertheless, the randomness lies in IDR resources and renewable generation in the ICES configuration issue still lacks thorough analysis. Furthermore, the widely focused concern of resiliency urges more proactive consideration of potential emergencies at the planning stage of ICES. To this end, this paper proposed a resilience-oriented stochastic ICES configuration framework considering IDR influence. First, generalized IDR models are set up in detail with elaborate fuzzy feature analysis of price-responsive multi-energy loads. Then, the vulnerability indicators of tie lines and converting devices of the ICES are first introduced to demonstrate the occasional outage in the normal operation and blackout in the emergent case. In addition, the worst-case conditional value-at-risk (WCVaR) theory is innovatively integrated to the traditional risk-neutral model, which is formulated as a two-stage stochastic chance-constrained programming problem, aiming to combine portfolio with minimizing the worst-case cost caused by a disaster. The models are then transformed into mixed-integer linear programming problems via several linearization techniques. Finally, the results of the case studies showed the established model’s superiority in improving the overall economy (reduce 6.94% of the worst-case cost) and system resiliency (decrease the value-at-risk by 3.20%) against an unforeseen hurricane. Meanwhile, the ICES reliability was also improved by declining 25.65% of unintended load curtailment faced with an emergency. The proposed approach provided an efficient preventive portfolio scheme of the IDR-integrated ICES for decision-makers’ different risk preferences of natural hazards.

Energy management in commercial building microgrids

Energy systems face problems such as increasing energy cost, greenhouse (GHG) emissions, and network load deviations. A wide range of load variations may cause network instability. Large commercial buildings with a heavy load, treated as microgrids (μGs), may contribute meaningfully to demand response and network stability. Therefore, a strategy for optimal energy consumption and bidirectional power trade between the building and the utility grid is required for this class of customers. The objective of this paper is to develop a linear bi-level macro energy hub (MEH) control for a group of large commercial building μGs, to minimize the network load deviation, while satisfying the customer specified total μG energy cost and the GHG emission constraints. The resulting constrained optimization problem is mapped in a flower pollination algorithm environment under deterministic and uncertain conditions such as the random presence of solar irradiance and electric and natural gas networks. The μG considers the solar photovoltaic panel, internal combustion engine, fuel cell, microturbine, and electric vehicle lot and battery. Using the results obtained under deterministic conditions, customer-utility relationship is developed by applying the multi-linear regression analysis technique, between the network load deviation, operation and maintenance cost of distributed generators, μG's spinning reserve value, and total μG energy cost. Analysis proves that under an MEH, customer-utility interest is non-linearly related. Building owned micro energy hub control reduces the energy cost and the GHG emissions; however, the load deviation inflates and vice versa for utility owned MEH control. To address this nonlinearity, biogas is integrated with the MEH, using a method termed as integrated demand response (IDR), which successfully brings the energy cost down at a low load deviation. The results show that if uncertainties are not severe, the IDR alone will be adequate to bring the cost down for simultaneous achievement of end-to-end stakes. However, if the cost is high, due to adverse conditions, in addition to the IDR, the proposed load curtailment scheme is applied. Simulations demonstrate that the cost, GHG emission, and load deviation drop by 22.448%, 11.716%, and 36.410%, respectively, for biogas integrated MEH compared to building owned micro EH control.

Stochastic Operation Framework for Distribution Networks Hosting High Wind Penetrations

In this paper, a stochastic framework including two hierarchical stages is presented for the operation of distribution networks with high penetrations of wind power. In the first stage termed Day Ahead Market Stage (DAMS), the power purchases from the day-ahead market and commitment of distributed generations (DGs) are determined. The DAMS model is formulated as a mixed integer linear programming optimization problem. The uncertainty in predictions of wind generation, real time prices, and load profile are included in the optimization problem according to a scenario-based stochastic programming approach. The risk encountered due to the uncertainties is also taken into account. The objective is to minimize the expected operation cost while satisfying the acceptable level of risk. In the second stage named Real Time Market Stage (RTMS), the power purchases from the real time market, dispatch of committed DGs, load curtailment invocations, and hourly reconfigurations are determined. In each hour, the RTMS problem is solved based on the information of that hour and next few hours. To prevent large numbers of switching operations during a day, the switching cost of reconfiguration is considered. The RTMS is modeled as a mixed integer conic programming problem. To analyze the proposed framework, the IEEE 33-bus DN is used as a case study.

Negotiation Decision between Manufacturer and System Operator on the Electric Energy Demand Response Program

In the electricity market, the imbalance issues between the demand and supply have been an ongoing challenge, especially during the on-peak period. Generally, the system operator adopts the demand response programs: the price-based and incentive-based program, to promote the manufacture decrease the demand during the on-peak period. However, as the manufacturer’s consumption pattern varies from the load curtailment strategy to the load deferral strategy, the system operator should set appropriate price or rebate levels to face the manufacturer’s different choices. The manufacturer announces the portion of on-peak demand for demand response program as well as the choice of the energy consumption patterns in advance. Moreover, the system operator (she) decides the price or rebate level accordingly. To analyze the negotiation process, we formulate a Stackelberg game, where the manufacturer, acting as the leader and the system operator is the follower. The results show that the incentive-based program is more useful to promote the manufacturer to provide a higher portion to the demand response program. However, no dominated program ensures higher profit for the system operator or the less cost for the manufacturer. So, it is the unit shifting cost and customer’s sensitivity to the rebate level that determines the system operator’s preference.

A two-stage optimal energy management by using ADP and HBB-BC algorithms for microgrids with renewable energy sources and storages

Abstract Energy management system (EMS) plays a key role to control and maintain efficiency and reliability of micro-grids (MGs) at satisfactory levels. In this paper, a two-stage optimal EMS is designed for MGs with renewable energy sources and storages, and residential and industrial loads, with the capability to predict stochastic electrical demands and electricity prices with respect to operational and model uncertainties. A two-stage optimization method, based on Approximate Dynamic Programing (ADP) and Hybrid Big Bang Big Crunch (HBB-BC) algorithm, is developed to optimally manage power generation, distribution and saving within MGs. Conditions of the main grid, operational cost functions are fully addressed into the design. In particular, Time of Use (ToU) tariffs and curtailable loads are considered to implement a Demand Response (DR) program to efficiently enhance the optimization performance. The proposed method is applied to a residential MG benchmark system, and simulation results are discussed and compared in various scenarios.

Alleviating congestion of hybrid urban grid by VSC‐control and AC‐line switching

As electricity load in large cities overgrows these years, mainly due to the increasing penetration of electric vehicles, the urban transmission networks are subject to the high risk of congestion because of insufficient planning. Reconfigure the high-voltage distribution network (HVDN) is an efficient way to mitigate the urban congestion. However, the discrete load transfer amount is not precise that might cause load curtailment, and the computation burden is also heavy when the system scale is enormous. In recent years, the emergence of voltage source converter (VSC) as the core components of DC transmission technique provides a new control measurement featuring fast and flexible. Nevertheless, its adjusting capacity is limited and subject to the system topological structure. Thus this paper proposed a coordinative control model that combines the load transfer capability of the HVDN and flexible and precise control of VSC to alleviate the urban transmission congestion. Numerical results showed that the congestion lines could be efficiently relieved thus defer further investment in the transmission network.