Load Curtailment Research Articles

Optimization Technique for Renewable Energy Storage Systems for Power Quality Analysis with Connected Grid

Power quality is the main problem with the power system network. Poor electricity quality may cause disruptions and financial challenges for consumers. Additionally, it could cause electronic gadgets to overheat, be damaged, or operate inadvertently. Transformers and other components of the power distribution system may also overheat and experience core saturation. This study investigates potential problems with the quality of the electricity in a photovoltaic linked power system. This paper suggests a new optimization method for day-ahead trading and control in DC microgrid power management (MG). The goal of the multiobjective optimization dispatch (MOOD) problem is to lower overall operational costs as well as the costs associated with power loss in efficient conservation systems and exhaust emission quantities such as nitrogen oxides, sulphur dioxide, and carbon dioxide. Using the weighted sum approach, the multiobjective optimization problem is reduced to a single optimization problem. The analytical hierarchy process (AHP) method is then used to calculate the weight coefficients while accounting for each objective function’s preferences. Power balancing, high levels of renewable energy penetration, the most effective scheduling of battery charging and discharging, control of load curtailment, and the technical limitations of the system are all taken into account when evaluating the system’s performance in both grid-connected and standalone operation modes. The ant lion optimizer (ALO) technique is taken into account to tackle MOOD, comparing the effectiveness of the proposed method to other well-known heuristic optimization techniques. The simulation’s results demonstrate how effectively the proposed strategy can address the coordinated control and optimization dispatch difficulties. They also found that operating the MG system economically in grid-connected mode can save overall costs by roughly 4.70% compared to doing it in independent mode.

Reliability Assessment of Demand Response Strategies for Profit Maximization

The idea of renewable resource integration influences the demand-supply balance significantly. The effort taken to diminish the deviation in demand-supply balance is greatly influenced by demand response (DR) strategy. In this analysis, the concept of DR is incorporated via the approach of load management. The optimal scheduling of the load considering the available energy helps to avoid unessential load shedding. The DR strategy of load shifting is suggested at different levels in accordance with the nature of load and real-time price. The profit of the utility is enhanced by providing the customer the flexibility to schedule their load. The curtailment of load can be avoided by increased customer participation in the suggested DR programs. The operating system is designed to be both efficient and effective by utilizing the real-time tariff profile of electricity to plan the generation of renewable energy, energy storage systems that include electric vehicles, and the utilization of grid power. The scalability and generalization of the proposed DR approach make it suitable for large-scale power systems that integrate renewable energy sources. The validation of the proposed method using reliability analysis ensures the effectiveness of the formulated DR. The proposed method is tested under two different scenarios and topologies on the IEEE-33 bus system and the results accomplished are promising.

Operational risk assessment on power system based on weather regionalization considering power ramp of renewable energy generation

With the ever-increased installed capacity of renewable energy generation (REG) units, the power ramp of REG has become a common event, introducing an operational risk to the power system. In order to quantify the operational risk, this paper proposes a novel method for assessing the operational risk of power systems with a high proportion of renewable energy, taking into account the power ramp of REG. Firstly, a weather regionalization is proposed to divide the region of the power system into multiple areas, and then the weather data from the weather stations in these areas will be utilized to forecast the power of REG probabilistically. Moreover, an operational risk assessment model based on the cumulative prospect theory is built to reflect the irrational psychological factors of dispatchers, and two typical indices are introduced to show the operational risk of renewable energy curtailment and load curtailment caused by the power ramp of REG. Finally, numerical simulations are conducted in the actual CEPRI-RE power system in the northwest region of China, and the actual weather data from the meteorological agencies is used. The results show that the proposed method is able to assess the operational risk of the power system with a high proportion of renewable energy effectively, and achieve the early warning 30 min in advance. Besides, it can be found that the power system at the turning point between the power ramp and normal operation has a higher operational risk.

Multi-Segment Decentralized Control Strategies for Renewables-Rich Microgrids in Extreme Conditions

Microgrids provide a promising approach to accommodating various distributed energy resources (DERs), while requiring significant communication infrastructures that may be affected by extreme conditions such as natural disasters and cyber-attacks. In this paper, a fully decentralized control strategy without the need for communication is proposed for islanded microgrids with high renewables penetration. First, special multi-segment power/frequency characteristic curves are designed, so that different DERs can be automatically coordinated in a prioritized manner such as renewables first to maintain power balance while DER frequencies are regulated at their reference values. Second, piecewise linear served load versus frequency models are designed to prioritize loads according to their significance, so that only noncritical loads will be curtailed as needed while critical loads are supplied without any interruptions during power deficiency. The proposed strategy can effectively deal with various normal and extreme system conditions including 100% renewables penetration, loads and renewables variations, power deficiencies requiring load curtailments, disconnection of existing DERs, connection of new DERs as well as network sectionalization and reconfiguration. The proposed control strategy is validated in the real-time digital simulator (RTDS) model of the IIT Campus Microgrid to demonstrate its effectiveness in enhancing the resiliency of renewables-rich microgrids in extreme conditions.

Optimal resilient microgrids formation based on darts game theory approach and emergency demand response program for cyber-physical distribution networks considering natural disasters

In this paper, novel multi-objective microgrids (MGs) formation method-based darts game theory optimization algorithm is proposed. The main objectives are to minimize the total losses, amount of load-shedding, and total restoration cost while ensuring a variety of topological and electrical constraints. The network graph theory represents the formed MG, and the non-linear equations of power flow and loss calculations are adopted in the MG formation model. In the proposed model, the DRERs include diesel generators, fuel cells, micro-turbines, and renewables wind and PV units with energy storage systems. The demand response program includes curtailable loads to improve load profile reduction during peaks. To assess the system's resiliency under extremely inevitable incidents, resiliency metrics are applied. To validate the effectiveness of the proposed approach, the modified IEEE 33-bus test system is utilized. Single-fault and multiple faults disaster case studies are performed with and without considering the emergency demand response program (EDRP). The results show the effect of EDRP in minimizing system losses, load-shedding, restoration cost, and improving the resiliency metrics after natural disasters. Finally, the analytic hierarchy process (AHP) model indicates the most efficient operation scenario while achieving the system objectives considering the problem alternatives.

Risk-averse restoration of coupled power and water systems with small pumped-hydro storage and stochastic rooftop renewables

Modern coupled power and water (CPW) systems exhibit increasing integration and interdependence, which challenges system performance to disasters and makes service restoration complex during post-disruption. Meanwhile, new technologies, such as small pumped-hydro storage (PHS) and rooftop renewables, are being widely installed and further deepen the interdependencies. To capture these features and improve overall performance, this paper proposes a coordinated restoration framework for a CPW system to respond to disruptions. The proposed CPW model comprises physical networks and mechanisms, considering available units, such as water desalination/treatment plants, pump stations and small PHS, in the water system, and rooftop renewables, distributed generators, in power system. The interdependencies are modeled through component-wise connections and consumer behavior, then grouped into three phases: production, distribution, and consumption. Aggregate service loss with respect to different consumer loads and time periods, is chosen as performance metric and to be minimized using network reconfiguration, energy/water dispatching, load curtailment, and operation management of components. A two-stage risk-averse stochastic programming is applied for reliable restoration and manage risks, to tackle the uncertainties in renewable power generations and water/power demands that affect method effectiveness. Finally, the method is implemented on a modified 33-bus/25-node CPW system, and the results demonstrate the effectiveness of the proposed restoration framework.

Short-term energy scenario of district energy system using optimised renewable energy mix with and without energy storage

The study proposes a multi-objective, multi-node optimisation model for the district energy system to achieve high renewable energy share by 2030. A combined analysis is conducted to reduce total system cost, carbon footprint; and satisfy inertia and flexibility requirements of integrated renewable energy system of the considered district. An hourly deterministic tool, Flextool, developed by IRENA, is used for analysis. Multiple scenarios are analysed to identify an optimal capacity expansion plan. The interaction between various energy generation units while meeting hourly energy demand is studied. The flexibility issues concerned with high VRE (Variable renewable energy) integration expected in the future energy grid are identified, and the effect of battery storage on curtailment is evaluated. The maximum VRE absorption capacity of the district is examined, and the shadow value is calculated for all considered scenarios. It is concluded that the district energy system could achieve 52% renewable energy share, zero loss of load and minimum curtailment in 2030 with adequate policy interventions. If the recommended investment pattern is followed, carbon intensity will reduce from 10.729 g/kWh to 6.70 g/kWh and 6.02 g/kWh without and with battery storage systems. The application of battery storage as a flexibility solution resulted in a significant reduction in curtailments. The study demonstrated that Flextool is a competent flexibility assessment tool which provides comprehensive support to optimise capacity expansion and system operations.

A tri‐level optimization model for power systems defense considering cyber‐physical interdependence

AbstractTo seek the optimal defense strategy against malicious attacks, this paper proposes a novel defender–attacker–operator model applicable to the cyber‐physical power system. The proposed model considers the interdependence in functionality and topology of the cyber network and the power network as well as the cyber network's constraints. The defender aims to minimize the load curtailment caused by the attacker, while the latter intends to maximize the power loss. The operator in the bottom level takes corrective action to minimize the attack consequence. This tri‐level model is decomposed into a master problem and a subproblem, based on which the column‐and‐constraint generation algorithm is implemented to obtain the optimal solution. Comparative case studies based on IEEE RTS‐79 system are carried out to demonstrate the advantage of the proposed method, and to investigate the impact of the energy coupling strength, the topologically independent link and the defensive resource. The effectiveness of this model is validated by the sensitivity analysis.

Coordinating the day-ahead operation scheduling for demand response and water desalination plants in smart grid

Integrating renewable energy resources (RES) is a challenge for power system operators due to their fluctuations and unpredictability. At the same time, the water shortage problem and the needs to desalinate more freshwater increase the prominence of sufficient energy resources for sustainable operation. Therefore, this paper presents a market-clearing mechanism in a co-optimization model that coordinates the operation of grid-connected reverse osmosis water desalination plants (RO-WDPs) and the operation of renewable-rich power systems. It is assumed that electric demands can participate in the provision of demand response (DR) to the market via multiple DR options. The DR options related to the general loads are demand shifting, load curtailment, distributed generation, and hybrid energy storage systems (HESS). These HESS includes battery storage and hydrogen storage systems. Pertaining to their special characteristics, a new DR option is proposed as a customized option for RO-WDPs. The market clearing mechanism is assumed to be based on the security-constrained unit commitment (SCUC) and formulated as a stochastic mixed-integer linear programming problem (MILP) to optimally schedule the day-ahead operation of the power system. The presented model is applied on 6-bus and IEEE 24-bus reliability test system (RTS) test power systems with significant penetration of RES to demonstrate its merits. The simulation results show that the system efficiency is enhanced by adding the energy flexibility of RO-WDP without endangering the water demand-supply when using the proposed coordinated model. Hence, the total operation cost is minimized, the RES integration is facilitated, and the hourly electricity prices are smoothened.

Home energy management system for enhancing grid resiliency in post-disaster recovery period using Electric Vehicle

Natural disasters such as earthquake, tsunami, storm etc. can cause power outages and limitations on the grid. Moreover, power limitations or outages up to 24 h can be applied to the disaster area as a precaution, even if there is no damage in the buildings and power system components. The reflection of the power limitations or outages can be vital for sufferers in post-disaster conditions. However, their impacts can be prevented by using electric vehicles as a mobile source in home energy systems. Electric vehicles are offering a promising technology as mobile emergency power source which ensure energy availability for critical demands in disaster conditions. Therefore, in this study a dynamic home energy management system (HEMS) algorithm that classifies all the appliances according to their importance in post-disaster conditions is proposed to enhance grid resiliency via preventing energy interruption of residential buildings. The algorithm utilizes efficient usage of local renewable energy sources (RESs) and electric vehicles (EVs). Load curtailment methods are applied considering the worst load consumption for the next 24 h according to the prospective remaining energy (PRE) of the EV battery (EVB). The main objective of the proposed algorithm is to arrange the permitted loads dynamically according to their importance levels in order to prevent energy interruption of a home. The proposed algorithm is verified with case based scenarios in MATLAB®/Simulink® environment by using experimentally gathered load and weather condition data. The energized time of the critical loads are prolonged up to %241 with the proposed algorithm.

Real-Time Simulation and Energy Management Attainment of Microgrids

The rapid spread of Microgrid systems has led to the need for an intensive analysis of the system to avoid several challenges such as stability, reliability, power balance, and other aspects. In this context, real-time simulation plays a vital role in the overall system study before the actual implementation stage. This helps avoid many on-site problems of the Microgrid by simulating the system and studying different operation scenarios. This paper uses the OPAL-RT simulator to perform a real-time simulation of an MG case study. Furthermore, it examines the implementation of the Fault Ride Through technique to overcome the total disconnection of the PV system following unpredictable faults. Moreover, a Load curtailment solution method is proposed in this study, to meet the balance and stable operation of the MG. The results prove the effectiveness of both techniques, with FRT implementation reducing the losses by about 62%, and the Load curtailment algorithm maintaining the balance of the MG.

Optimal load shedding scheme using grasshopper optimization algorithm for islanded power system with distributed energy resources

In this paper, a new optimal load shedding method using a grasshopper optimization algorithm (GOA) is proposed for maintaining the stability of the islanded power system that comprises distributed energy resources (DER). The GOA is used in conjunction with the method of voltage stability margin (VSM) to handle the multi-objective shedding constraints, namely the generation restrictions, allowable load curtailment, and load priority. To assess the effectiveness of the offered method, a comprehensive evaluation study is applied to a system of IEEE 33-bus and four DG units in view of different scenarios. Furthermore, the effectiveness of performance of the GOA-based load shedding is compared in terms of fitness value, voltage stability margin, and percentage of load that should be curtailed with three well-known optimization approaches (i.e., the particle swarm optimization (PSO), grey wolf optimization (GW), and genetic algorithm (GA)) under different islanded scenarios. The obtained results show that the performance of the proposed method is better than other methods with the less value of load curtailed for totally islanded states (i.e., 45.67% for island A, 16.63% for island B, 38.05% for island C, and 27.98% for island D in this study). In addition, the results of the simulation display the efficacy of the proposed scheme to ensure voltage stability with the optimal load quantity to be shed for dissimilar islanding scenarios.

DER Forecast Using Privacy-Preserving Federated Learning

With the increasing penetration of distributed energy resources (DERs) in grid edge, including renewable generation, flexible loads, and storage, accurate prediction of distributed generation and consumption at the consumer level becomes important. However, DER prediction based on the transmission of customer-level data, either repeatedly or in large amounts, is not feasible due to privacy concerns. In this article, a distributed machine learning approach, federated learning (FL), is proposed to carry out DER forecasting using a network of Internet of Things (IoT) nodes, each of which transmits a model of the consumption and generation patterns without revealing consumer data. We consider a simulation study that includes 1000 DERs and show that our method leads to an accurate prediction of preserving consumer privacy, while still leading to an accurate forecast. We also evaluate grid-specific performance metrics, such as load swings and load curtailment, and show that our FL algorithm leads to satisfactory performance. Simulations are also performed on the Pecan street data set to demonstrate the validity of the proposed approach on real data.

Research on large-scale wind power consumption in the electricity market considering demand response and energy storage systems

Large-scale wind power integration brings great challenges to power system operation. The use of large-scale wind power in the electricity market has become a concern for many researchers. Demand response (DR) and energy storage systems (ESSs) play crucial roles in the consumption of large-scale wind power. In this paper, a detailed DR model is established, including price-based demand response (PBDR) and incentive-based demand response (IBDR). IBDR contains load shifting (LS) and load curtailment (LC). The IBDR model in this study not only includes its bidding and market clearing but also contains relevant constraints: maximum/minimum duration time, shifting/curtailment gap time, and shifting/curtailment frequency. A two-stage trading method, including a day-ahead (DA) market and a real-time (RT) market, is proposed. The method contains various market participants: conventional units (CUs), rapid adjustment units (RAUs), wind power, ESS, and multiple types of DR. The roles and economic benefits of various market participants in the consumption of large-scale wind power are analyzed in an IEEE 30 bus system, verifying the accuracy and validity of the model. The best DR scale and the suggestions of ESS are given. The results show that the proposed method can effectively utilize wind power and decrease system costs.

Two-dimensional convolution-based power system reliability assessment

With the rise in carbon emissions, it has become fashionable to construct a new power system using new energy as the primary raw material for power generation. This, however, might result in a significant rise in the number of system states and frequent changes in the system state. These issues raise the bar for the effectiveness of system dependability evaluation. Traditional evaluation procedures necessitate computing Optimal Power Flow (OPF) for many systems states one by one, which is unquestionably time-consuming. For that purpose, this research proposes a reliability evaluation approach based on two-dimensional convolutional networks. Branch circuit fault messages are analyzed with generator fault messages. Multiple channel state data from power system node admittance matrices, power generation, and energy demand are fed into a two-dimensional convolutional neural network. This is used to determine the regression connection between load curtailments and system status data and to forecast load curtailments. Finally, it is applied to the RTS-79 system, and experimental results demonstrate that the technique has certain advantages in terms of computing speed and accuracy.

Refining long-term operation of large hydro–photovoltaic–wind hybrid systems by nesting response functions

The traditional long-term operation models of hydro–photovoltaic (PV)–wind hybrid systems (HPWHSs) were formulated on the basis of monthly or ten-day time-scale, and they failed to describe intraday stochastic and fluctuating features of the PV and wind power, resulting in sub-optimal operating rules. To address this issue, we proposed an effective method to refine long-term operating rules for the large HPWHS by nesting a set of response functions. These response functions were derived based on numerous daily operation results, characterizing relationships between long-term hydropower output and residual load, operational risk, and electricity curtailment metrics, respectively. A large HPWHS located in the upper Yellow River basin, Qinghai province, China was selected as a case study. Results showed that the refined operating rules could decrease curtailment rate of the new energy from 17.5% to 9% compared with a standard operation policy. Meanwhile, energy production of the HPWHS and its generation guaranteed rate were increased by 7.7% and 72.7%, respectively, and the residual load fluctuation and operational risk rate were reduced by 35.6% and 23.3%, respectively. Therefore, the proposed long-term operation method is effective for guiding the complementary management of the large HPWHS.

Modeling Energy Consumption in the Production Processes of Industrial Units Based on Load Response Programs in the Energy Market

The optimal operation of microgrids is of great significance for the sake of efficient and economical management of its energy resources. The microgrid energy management system should plan to operate the microgrid while simultaneously considering the electric and thermal load. The present study proposes energy management to minimize the costs of operating an industrial microgrid. In fact, planning for energy supply is among the critical issues that distribution companies deal with daily in the competitive environment. A distribution company usually meets customer (end customer) demands by purchasing energy from a wholesale market. Given the load curtailment, distribution companies have more choices and interactions in the market. Distribution companies face the two uncertainties of load changes and price fluctuations in their daily energy supply planning which could lead to the risk of loss resulting from the distribution company’s decision-making for daily energy supply planning. Thus, these companies face the challenge of maximizing profit in a risk-based environment. Therefore, the present study presents an optimal model of energy consumption in the production processes of aluminum and cement industrial units. The presented model was then used in planning the day-ahead energy of a microgrid containing these industrial units. Since the studied subject has many limitations, it would be difficult to solve it using mathematical methods. To resolve this issue then, the present study introduces a newly developed algorithm inspired by bee colonies. The proposed method seeks to significantly improve in the local and global search capabilities. In addition to confirming the validity of the proposed model, results indicate that the implementation of load-response programs and the cooperation of industrial units in the ancillary services and energy market can increase the profits of units and microgrids as well as correct the demand curve. According to the obtained results from the first and second test cases, the total profit of the aluminum unit was $188,103 and $237,805, respectively. Similarly, this profit for the cement industrial unit is $104,350 and $233,195.3, respectively. From the results, it can be observed that the final profit of the second unit has increased by 61%.

A DYNAMIC SIMULATION MODEL FOR OPTIMAL DEEP-LEVEL MINE COOLING MANAGEMENT AND OPERATIONAL DECISION-MAKING FOR ESKOM’S LOAD CURTAILMENT

The South African mining sector depends on intensive energy usage to extract minerals. Most of this energy is used to enhance underground working conditions through cooling. Electricity costs have been on the rise, reducing the profitability of mines. Mines must invest in management systems that optimise energy usage while maintaining underground conditions. Dynamic simulation models are critical for systems analysis, optimal management, and improved decision-making. This paper presents an integrated dynamic simulation model for deep-level mine cooling systems. The model was developed using Process ToolBox simulation software, and a Mean Average Percentage Error of 3.53% was achieved. The impact of load curtailment on underground services was simulated. Eight working areas exceeded the legal limit of 32.5 °C, and the steady state simulation underestimated the impact, as only three working areas were affected. The simulation was sensitive to the ambient conditions; high temperatures were noted in the working areas when the ambient temperatures peaked.

Reserve Evaluation and Energy Management of Micro-grids in Joint Electricity Markets Based on Non-Intrusive Load Monitoring

The heating, ventilation, and air-conditioning (HVAC) units are regarded as major demand response (DR) resources in micro-grids. However, due to privacy concerns and technical constraints, it is difficult for the system operator to obtain the complete information on each individual appliance. In this paper, we present a non-intrusive load monitoring (NILM) based framework for the operation strategy of the micro-grid in the joint energy, reserve, and regulation markets. The NILM technologies enable the operator to disaggregate the power of the HVAC units from the reading of the smart meters. Hence, the operation state of the appliances and the behavior of consumers can be studied without obtaining detailed data of each individual appliance. An advanced NILM algorithm is proposed, and the Coupled Generative Adversarial Networks (C-GAN) are utilized to enhance the generalizability of the trained model. Based on the NILM result, a novel method based on Hidden Markov Model (HMM) is proposed to evaluate the upward and downward reserve capacity of the HVAC units. The evaluated reserve capacity can help the operator better bid in the joint market based on the proposed optimization model. The proposed framework and methodology are verified through case studies. The simulation result reveals that with NILM, the market operator can save more energy consumption costs and load curtailment costs and earn more revenues in the joint market by selling excessive energy and providing ancillary services.

Developing a Robust Expansion Planning Approach for Transmission Networks and Privately-Owned Renewable Sources

Power system restructuring has changed transmission expansion planning (TEP) and caused many complications due to conflicting and contradictory objectives. The transmission capacity expansion would significantly affect the revenue of investor-owned renewable energy sources (RESs). Thus, the investment decisions on merchant RESs must be considered in the TEP studies conducted by the transmission system operator (TSO). In this regard, this paper aims to propose a multi-objective co-planning of investment in transmission networks and merchant RESs with three objective functions: minimizing the investment cost of newly deployed transmission lines, minimizing transmission congestion cost, and minimizing load curtailment in N-1 conditions. Moreover, the TSO guarantees a desirable rate of return for private investors to finance renewable energy projects. Further, a robust optimization (RO) technique is employed to cope with the uncertainties associated with demand and renewable energy production. Also, a posteriori multi-objective optimization algorithm, i.e., the non-dominated sorting genetic algorithm (NSGAII), is applied to solve the advanced optimization problem, followed by the fuzzy min-max method to acquire the final optimal solution. Finally, the IEEE RTS 24-bus test system is utilized to demonstrate the effectiveness and applicability of the suggested approach.