Load Curtailment Research Articles

An Advanced Optimization Based Simulation Model to Study the Impact of Cyber-Physical Attacks on Power Systems

The promotion of energy internet causes external information to directly or indirectly affect power system control decisions through various business approaches. The interaction mechanism between power network and information network becomes increasingly complex. Modern power systems become more prone to cyber-attacks and physical attacks because of the high integration of information layer and physical layer. This paper provides an insight into the impacts of cyber and physical attacks on power systems, where the attacks are modeled in the form of mathematical (optimization) equations representing the attacks. Moreover, the cyber and physical attacks are modeled in the form of Mixed Integer Linear Programming (MILP) problem. The authors have simulated cyber-attack on transmission lines and cyber-physical attack on both transmission lines and loads. The MILP problem is solved by commercial solver, CPLEX. A case study on a modified IEEE 14 bus test system is considered to demonstrate the results of this research. Simulation results on the test system show that the cyber and physical attacks on power systems could cause undesirable load curtailment and mitigation of such attacks becomes essential for secure operation of the electric grid. This research would enable the power system operators to understand the potentially damaging effects of cyber-physical attack and advance their knowledge about such attacks so that prevention and mitigation of attacks on the power systems is possible.

Long term transmission expansion planning to improve power system resilience against cascading outages

A Transmission Expansion Planning (TEP) problem is proposed to find the optimal configuration of the transmission network with considering security and resilience constraints. Due to the importance of blackouts and cascading failure, the proposed Resilient TEP (RTEP) minimizes the effects of cascading outages in term of load curtailment. In order to estimate the size of cascading outages, based on the total lost load, an iterative algorithm is proposed to analyze and simulate the steady state mechanism of cascading outages. A set of initiating events are defined as the triggering points of the cascading failure and the consequent chain of related outages are identified to determine the blackout size. In addition to the resilience constraints, the security constraints based on N-1 security criterion are also considered. Based on the Benders Decomposition (BD) algorithm, a multi-stage solution procedure is developed to handle the investment decisions, security constraints, and resilience requirements efficiently. In order to achieve the optimal resilient TEP configuration, all the master and sub-problems are formulated as Mixed Integer Programming optimization models. The proposed RTEP is implemented over the IEEE 24-bus test system.

Reliability assessment of composite power systems containing sea wave slot‐coned generators

The generated power of the Sea-wave slot-coned generator (SSG), as one of the high capacity wave energy converters, is dependent on the period and height of the waves. The generated power of this energy-converter changes due to the variation in these parameters. Thus, different aspects of the power systems may be affected that must be investigated by increasing the share of these converters for electricity production in the power-systems. In this paper reliability assessment of composite power-systems containing large-scale SSG is performed. The Xie and Beni (XB) index is calculated for obtaining the optimum number of states in the reliability model of the SSG and fuzzy c-means clustering technique is proposed for reducing the number of states of the model. The contingency analysis method is proposed for calculating the reliability indices of the composite power-system incorporating large-scale SSG. DC load-flow method, which considers line capacity, is used to perform load-flow in the composite power-system including generation and transmission networks. In the proposed technique, a load-shedding program with the goal of minimizing the value of lost load is solved using of the linear programming method based on the interior-point-approach for contingency states leading to the load curtailment.

An Efficient Framework for Adequacy Evaluation through Extraction of Rare Load Curtailment Events in Composite Power Systems

With the growing robustness of modern power systems, the occurrence of load curtailment events is becoming lower. Hence, the simulation of these events constitutes a challenge in adequacy indices assessment. Due to the rarity of the load curtailment events, the standard Monte Carlo simulation (MCS) estimator of adequacy indices is not practical. Therefore, a framework based on the enhanced cross-entropy-based importance sampling (ECE-IS) method is introduced in this paper for computing the adequacy indices. The framework comprises two stages. Using the proposed ECE-IS method, the first stage’s purpose is to identify the samples or states of the nodal generation and load that are greatly significant to the adequacy indices estimators. In the second stage, the density of the input variables’ conditional on the load curtailment domain obtained by the first stage are used to compute the nodal and system adequacy indices. The performance of the ECE-IS method is verified through a comparison with the standard MCS method and the recent techniques of rare events simulation in literature. The results confirm that the proposed method develops an accurate estimation for the nodal and system adequacy indices (loss of load probability (LOLP), expected power not supplied (EPNS)) with appropriate convergence value and low computation time.

Characteristic matching of stochastic scenarios and flexible resource capacity optimisation for isolated microgrids

Considering a large number of stochastic scenarios when optimising the flexible resource capacity for isolated microgrids not only improves the accuracy and credibility of the results but also enables the use of probabilistic statistical methods to obtain a scheme that balances robustness and costs. However, the consideration of massive scenarios can drastically lower the computational efficiency, and there is a lack of research on configuration methods that can result in a compromise. In this study, a characteristic-matching method is proposed to enhance the computational efficiency of optimisation, and a statistics-based prism filtering method is designed to obtain the compromise scheme for use by decision-makers. Specifically, the massive scenario set is divided into four subsets. The characteristic-matching method is proposed to obtain a near-optimal solution, which is used as the initial iteration value to accelerate the computational speed. Then, a characteristic-matching-based bi-level optimisation method is proposed to solve massive scenarios with high computational efficiency. Moreover, a prism filtering method is designed to select a compromise scheme with high economic benefits using scenario coverage, load curtailment, power curtailment and economy indicators. Simulation results verify the effectiveness of the proposed models and methods.

Demand-Side Participation via Economic Bidding of Responsive Loads and Local Energy Resources

The active participation of demand response (DR) resources into the wholesale market price formation and load dispatch process has the potential to stimulate demand-side flexibility. However, it is challenging for a market entity to utilize the DR resources for practical use. This is because day-ahead wholesale market-clearing prices are uncertain, and DR resources are heterogeneous. Furthermore, DR participation may lead to violations of the distribution system’s operational constraints. In this article, we propose an approach for an aggregator/load-serving entity (LSE) to profitably bid aggregated DR resources into the day-ahead wholesale market. The LSE requires an optimal bidding strategy that reflects the price elasticity of the aggregated retail loads to participate in the wholesale market operations. In the proposed approach, the LSE executes load curtailment and load shifting contracts with DR resources, where DR resources are remunerated for their participation at pre-contracted incentive prices. Then, the LSE aggregates the DR flexibility and optimally bids it in the day-ahead wholesale market. The proposed approach is validated using the IEEE-123-bus test system. It is demonstrated that the LSE can successfully generate economic bids for its participation in the day-ahead market by optimal management of DR resources and without violating the network’s operating constraints.

A Coordinated Performance of Power System Operated with Participants in Demand Response Programs Considering Environmental Pollution Constraints

In a re-structured environment, demand response (DR) programs are presented in a new style. The demand response program consists of demand-side management methods which answers to electricity price variations. With advent of electricity markets, the demand-side management programs was introduced into two categories: (1) energy efficiency program, (2) demand response program. This paper studies engagement of participants load in an electricity market having a grid with large and small loads interested to participate in the market, energy storage systems, micro-grids, and distributed generations. In the proposed scheme, it is supposed to have an aggregator which sends demand-side preferences including load curtailment, load shifting, onsite generation, and energy storage systems along with proposed value and prices and all respective system constraints to the independent system operator (ISO). In fact, load aggregators submit aggregated DR offers to the ISO in order to optimize final decisions on aggregators’ DR contributions in wholesale market. The main goal of this paper is to solve the operation problem considering demand-side, system, and pollution and reliability constraints. Compared to other methods, the results indicate that the explicit modeling of customer DR would provide ISOs with more flexible options for scheduling the available energy resources in day-ahead energy markets.

A Mathematical Model for the Scheduling of Virtual Microgrids Topology into an Active Distribution Network

This article presents a method based on a mathematical optimization model for the scheduling operation of a distribution network (DN). The contribution of the proposed method is that it permits the configuration and operation of a DN as a set of virtual microgrids with a high penetration level of distributed generation (DG) and battery energy storage systems (BESS). The topology of such virtual microgrids are modulated in time in response to grid failures, thus minimizing load curtailment, and maximizing local renewable resource and storage utilization as well. The formulation provides the load reduced by bus to balance the system at every hour and the global probability to present energy not supplied (ENS). Furthermore, for every bus, a flexibility load response range is considered to avoid its total load curtailment for small load reductions. The model has been constructed considering a linear version of the AC optimal power flow (OPF) constraints extended for multiple periods, and it has been tested in a modified version of the IEEE 33-bus radial distribution system considering four different scenarios of 72 h, where the global energy curtailment has been 27.9% without demand-side response (DSR) and 10.4% considering a 30% of flexibility load response. Every scenario execution takes less than a minute, making it appropriate for distribution system operational planning.

Energy cost and efficiency analysis of building resilience against power outage by shared parking station for electric vehicles and demand response program

This paper considers two commercial and residential buildings for building energy resilience against natural disasters that cause a power outage. The buildings are modeled with a shared parking station for their electric vehicles. The peer-to-peer operation is modeled for the buildings. The electric vehicles inside the parking station have dissimilar patterns of availability and such dissimilarity helps the buildings to be benefited from the vehicles for extra hours. The power outage is modeled at different day hours and with various durations. The building is supported by energy management options to handle such disruptions. The options are a peer-to-peer operation of the building, electrical vehicle charging-discharging, partial charge ability, load curtailment, and load adjustment. The proposed model only utilizes available components of the buildings and it does not need to install further components. The purpose is to minimize energy cost and maximize energy resilience under natural disasters. The resilience is defined as critical load restoration and minimum energy loss under various power outages. The results demonstrate that the designated energy management options can practically minimize energy cost and improve energy resilience following blackouts. The electric vehicles can reduce energy cost by about 25% and supply the loads under 7-h power outage.

Risk-Averse Optimal Energy and Reserve Scheduling for Virtual Power Plants Incorporating Demand Response Programs

This article addresses the optimal bidding strategy problem of a virtual power plant (VPP) participating in the day-ahead (DA), real-time (RT) and spinning reserve (SR) markets (SRMs). The VPP comprises a number of dispatchable energy resources (DERs), renewable energy resources (RESs), energy storage systems (ESSs) and a number of customers with flexible demand. A two-stage risk-constrained stochastic problem is formulated for the VPP scheduling, where the uncertainty lies in the energy and reserve prices, RESs production, load consumption, as well as calls for reserve services. Based on this model, the VPP bidding/offering strategy in the DA market (DAM), RT market (RTM) and SRM is decided aiming to maximize the VPP profit considering both supply and demand-sides (DS) capability for providing reserve services. On the other hand, customers participate in demand response (DR) programs by using load curtailment (LC) and load shifting (LS) options as well as by providing reserve service to minimize their consumption costs. The proposed model is implemented on a test VPP and the optimal decisions are investigated in detail through a numerical study. Numerical simulations demonstrate the effectiveness of the proposed scheduling strategy and its operational advantages and the computational effectiveness.

Enhancing electricity market flexibility by deploying ancillary services for flexible ramping product procurement

The escalated penetration of wind energy in the electric industry has by large contributed to an intensified uncertainty and variability of the net-load, i.e., the difference between stochastic demand and intermittent supply. In response, some Independent System Operators (ISOs) have recently introduced the Flexible Ramping Product (FRP) to heighten the grid flexibility via effective hosting of the uncertain renewables. This paper suggests a novel framework for optimal FRP provision in real-time markets (RTM) in which the shortage in the RTM ramp capacity is compensated by optimal deployment of day-ahead procured spinning reserve. In the envisioned mechanism, the ISO can deploy specific portions of the spinning reserve capacity as the FRP price increases. On this basis, a new optimization algorithm is formulated that can best capture the joint ability of ESSs and thermal generating units in providing FRP in the RTM. The proposed model is numerically investigated on the IEEE 118-bus test system, where it reveals its efficacy and applicability in load curtailment reduction.

Goal-Programming-Based Multi-Objective Optimization in Off-Grid Microgrids

Renewable-based off-grid microgrids are considered as a potential solution for providing electricity to rural and remote communities in an environment-friendly manner. In such systems, energy storage is commonly utilized to cope with the intermittent nature of renewable energy sources. However, frequent usage may result in the fast degradation of energy storage elements. Therefore, a goal-programming-based multi-objective optimization problem has been developed in this study, which considers both the energy storage system (battery and electric vehicle) degradation and the curtailment of loads and renewables. Initially, goals are set for each of the parameters and the objective of the developed model is to minimize the deviations from those set goals. Degradation of battery and electric vehicles is quantified using deep discharging, overcharging, and cycling frequency during the operation horizon. The developed model is solved using two of the well-known approaches used for solving multi-optimization problems, the weighted-sum approach and the priority approach. Five cases are simulated for each of the methods by varying weight/priority of different objectives. Besides this, the impact of weight and priority values selected by policymakers is also analyzed. Simulation results have shown the superiority of the weighted-sum method over the priority method in solving the formulated problem.

Impact Analysis of Seismic Events on Integrated Electricity and Natural Gas Systems

Seismic events can cause devastating impacts on both overground and underground energy system infrastructure. This paper proposes a methodology to evaluate the impact of seismic events on the security of integrated electricity and gas system, mainly focusing on pipelines leakage and connection loss of electricity transmission lines. A stochastic model is used to formulate the damage level based on earthquake severity. The seismic impact on the integrated system is classified according to the levels of pipe leak and electricity line failure. Load curtailment due to limited generation capacity and overloaded transmission lines is thereafter quantified. Seismic intensity is generated randomly based on Monte Carlo simulation so that a certain seismic intensity can be related to relevant load curtailment. An integrated energy system with a 30-busbar electricity system and a 6-node natural gas network is used to demonstrate the effectiveness of the proposed method. The results clearly illustrate damage consequences under seismic events in terms of both probability and severity levels. This work can inform resilience enhancement scheme design based on the vulnerability performance and impact of both systems.

Floating PV system as an alternative pathway to the amazon dam underproduction

With increasing electric demand and the continued underproduction of existing dams and the environmental and social concerns of traditional hydropower dam expansion in the Amazon region, alternative power sources are needed to meet future power needs. Recently, large-scale deployment of photovoltaic (PV) systems has gained more interest globally, mainly because of the improved technology and rapid price decrease. Although solar power in Brazil makes up a small percentage of its generation mix, large-scale deployment could potentially be one of the promising solutions for offsetting dams' underproduction. This work evaluates the benefits of adding floating PV (FPV) systems on system adequacy. FPV systems are integrated alongside existing dams to enhance the existing power sources and provide an alternative pathway to meet the increasing power demand without the need for more dams. System adequacy is evaluated with the current production of dams and the required capacities of FPV systems to compensate for the current underproduction of dams. Furthermore, the correlation between PV output and system load is evaluated, and the environmental and social concerns associated with dam expansion in the Amazon basin are briefly discussed. The results indicate that the investment toward installing FPV systems on the dams’ reservoirs leads to a significant improvement to the overall system reliability, minimize load curtailment, and could potentially add more flexibility to the operator to dispatch power generated by hydropower plants during peak demands.

Analytical model of power system hardening planning for long-term risk reduction

Hardening components is an effective way to decrease the load loss risk caused by component outages in power systems. The system hardening against the long-term risk usually faces the computation burden problem brought up by the risk evaluation. This paper proposes a deterministic hardening planning model to minimize the long-term system risk of load curtailment, which eases the computational cost concern. To accomplish this task, a set of system events with load loss and corresponding load curtailments are obtained by an improved Monte Carlo simulation. An analytical function of system risk index with respect to the unavailability of components is derived and can calculate the risk index rapidly. Then, the deterministic model is constructed by applying the analytical function into the stochastic problem of system hardening planning. The greedy algorithm is developed to solve the model. The proposed model and method improve the computation efficiency by avoiding the repeated time-consuming risk evaluations. Case studies are conducted on the RBTS, RTS79, and a modified RTS96 system. Performances of the proposed model and method are compared with that of the conventional reliability optimization method (CROM). Results demonstrate that the model obtains better solution than the existing study on hardening planning for long-term risk, and has higher computation efficiency than CROM.

Distribution system restoration using graph theory after multiple faults

This study presents a novel graph theory-based approach to restore the distribution systems (DSs) after multiple simultaneous faults due to extreme weather conditions. The algorithm promptly finds the switching sequence to energise as many healthy zones as possible in an unbalanced DS. All the resulted topologies are later checked for operating constraints such as line flows and bus voltages. Hierarchy based load curtailment is proposed to remove any existing violations by shedding minimum non-critical loads. The proposed algorithm also gives the order in which the faults have to be cleared, depending on the priority of the disconnected load and inconvenience caused to the public. OpenDSS is used for unbalanced power flow analysis. Studies on the IEEE 37-bus system and the IEEE 123-bus system compared the proposed algorithm with other existing methods in the literature.

A mixed binary‐continuous particle swarm optimisation algorithm for unit commitment in microgrids considering uncertainties and emissions

Objective Using a mixed binary continuous particle swarm optimisation (PSO) with V-shaped quadratic transfer for solving UC in demand response integrated MGs, while the uncertainties are considered. Method A mixed binary continuous particle swarm optimisation (PSO) is used for optimisation. Results The UC results have been achieved for 6 different scenarios. The potential of load curtailment as an incentive-based demand response program has been approved in decreasing operation cost of MGs and the effect of emissions, spinning reserve constraint and bid functions on operation cost of MG has been investigated. Discussion The achieved results in different scenarios approve the outperformance of the proposed PSO over some benchmark optimisation algorithms.

Dynamic environmental‐economic load dispatch in grid‐connected microgrids with demand response programs considering the uncertainties of demand, renewable generation and market price

AbstractMicrogrids (MGs) as a key building block of smart grids have been emerged to address the proliferation of distributed energy resources. In grid‐connected MGs, dynamic economic load dispatch (DELD) module determines optimal schedule of distributed energy resources and adjustable loads and power to be exchanged with upstream grid, while all operational constraints of the MG are respected. DELD in MGs represents a constrained optimization problem with uncertain input data, as the forecasts of demand, renewable generation and market price are uncertain. In this research, particle swarm optimization (PSO) as a bio‐inspired optimization algorithm is used to solve DELD in grid‐connected MGs, while demand response program is integrated into MG and the uncertainties of demand, renewable power generation and market price are dealt with two‐point estimate method (TPEM). Load curtailment as a demand response program is used for reducing operation cost of microgrids. The performance of PSO is compared with two optimization algorithms including grey wolf optimization and backtracking search algorithm. As per the results, at times with low grid power price, microgrid imports power from upstream grid and at times with high power price it exports power to the upstream grid. The results show that the integration of demand response has significantly reduced the operation cost of the microgrid. The effect of change in maximum curtailable power on the operation cost of the MG has been investigated.

Particle Swarm Optimization for Computing Nash and Stackelberg Equilibria in Energy Markets

Interactions among stakeholders in deregulated markets lead to complex interdependent optimization problems. The present study is motivated by load control programs in energy markets and more precisely by using the power supply interruption as a tool for reducing consumers’ demand voluntarily, also known as voluntary load curtailment programs. The problem is formulated as a Stackelberg game, specifically, as a bilevel optimization problem that belongs to the mathematical programs with equilibrium constraints. In this game, a player that acts as leader determines the actions of the players that act as followers and play a Nash game among them through a subsidy program. The corresponding equilibria need to be found and the presence of nonconvex functions makes the use of metaheuristic algorithms attractive. An extension of particle swarm optimization is proposed for solving such problems based on the unified particle swarm optimization that is a variation of the plain particle swarm optimization algorithm. The proposed algorithm is tested by solving some examples of the formulated games in order to study its efficiency and the interactions between the stakeholders of the market.

Risk-constrained stochastic optimal allocation of energy storage system in virtual power plants

Abstract This paper aims to develop a decision-making procedure for efficient placement and sizing of energy storage system (ESS) within virtual power plants (VPPs) premises under the uncertainty of market price. The main aim is to minimize the overall cost of VPP within a fixed time horizon planning. The understudy VPP consists of wind turbine, photovoltaic system, curtailable loads, ESS, and diesel generators in which the VPP trades electricity with the upstream grid. The proposed framework investigates both optimal power and optimal energy of ESS based on the available budget for investment. To hedge against the uncertainty of investment, an efficient risk management manner based on the concept of conditional value at risk is applied. Moreover, from the reliability point of view, two reliability indices: the loss of load expectation and energy expected not served, are evaluated under different levels of investment to assess the impact of ESS on VPP reliability. The proposed model is formulated as a mixed-integer nonlinear programming problem and is solved by the well-known General Algebraic Modeling System commercial software package. The effectiveness of the proposed risk-based optimization approach is demonstrated through vigorous case studies and comprehensive cost-benefit analysis.