Interruptible Load Research Articles

Collaborative optimization for energy hub and load aggregator considering the carbon intensity-driven and uncertainty-aware

To achieve optimization operation between the energy hub operator and the load aggregators, this paper proposes a collaborative model for the energy hub and load aggregator considering the carbon intensity-driven and uncertainty. Firstly, a time-coupled carbon emission flow model for energy storage is proposed to cope with the change in carbon intensity at the injection node. Meanwhile, a carbon intensity-based incentive price is developed for interruptible loads with diverse carbon intensity. Secondly, the Wasserstein metric-based distributionally robust optimization method is leveraged to height against the energy hub uncertainty, in which the infinite-dimensional distributionally robust optimization model is reformulated into a tractable linear programming. Thirdly, considering the multi-entity characteristics of energy hub and load aggregators, a collaborative optimization method based on the alternating direction method of multipliers algorithm is constructed. Finally, to meet the convergence requirements of the alternating direction method of multipliers algorithm, the iterative convexification algorithm is applied to transform the nonconvex bi-level energy hub-users model into an iterative convex model by avoiding binary variables and big-M parameters. Consequently, the energy hub and load aggregator can operate cooperatively and protect privacy in uncertain conditions. Simulation results verify the effectiveness of the proposed model and method. Simulation results demonstrate the effectiveness of the proposed model and method. Specifically, the iterative convexification algorithm achieved a profit of ¥1653.82 for the load aggregator, which is 24.4 % higher than the profit obtained by CONOPT (¥1329.73) and 20.3 % higher than that obtained by IPOPT (¥1374.69), thus providing substantial advantages over traditional nonlinear solvers.

Optimizing multi-energy systems with enhanced robust planning for cost-effective and reliable operation

This paper introduces a comprehensive and resilient multi-energy system (MES) designed for independent planning and real-time implementation. A robust daily coordinated planning model is proposed, incorporating adjustable optimization with fundamental operational and uncertainty constraints. The model integrates various energy sources and systems, including photovoltaics, wind turbines, combined heat and power (CHP) units, energy storage system (ESS), electric vehicle (EV), electric boilers, and power-to-gas (P2G) facilities, to manage electricity, natural gas, and heat demands. The objective is to minimize MES operational costs while meeting electricity and heat requirements, considering renewable energy uncertainties. It includes the development of a two-stage flexible robust optimization model that accounts for energy equilibrium, capacity constraints, and demand response mechanisms. The model incorporates price-based demand response with both switchable and interruptible loads, enhancing system controllability and flexibility. Additionally, a scenario generation and reduction technique based on the Kantorovich distance is employed to effectively manage forecast errors and uncertainties. A novel modified Slime Mold Algorithm (SMA) is utilized to solve the optimization problem, demonstrating superior convergence and computational efficiency compared to traditional meta-heuristics. The slime mold algorithm is further enhanced with chaos theory, using a sine map to introduce dynamic exploration capabilities. The findings indicate that the proposed multi-energy system model effectively balances electricity, natural gas, and heat loads while accommodating renewable energy fluctuations. The enhanced slime mold algorithm provides optimal solutions swiftly, ensuring reliable and cost-effective multi-energy system operation.

On the Internationalization of English Teaching System in the Planning of Sustainable Development of Future Renewable Energy

Energy is an essential element of human production and business activities. At present, China's energy development and utilization is facing the contradiction between increasing environmental pressure and high emission energy consumption structure. Based on the optimal power flow algorithm, this paper optimizes the scheduling and international teaching of the power system with a high proportion of renewable energy. The probability density function of fan output and photovoltaic output is obtained by wind speed and light intensity model, and the probability density function of load is also proposed. Then, interruptible load, microgrid and energy storage are used as flexible resources to improve the system’s flexibility to meet the random fluctuations caused by a high proportion of wind and light, so as to maintain the stable operation of the power grid. The mathematical model of process optimization power flow satisfying process constraints is a nonlinear time-varying system. The key to process optimization power flow algorithm is to find a simple and effective linearization and discretization method that is in line with the actual power network. On the basis of ensuring the accuracy of the model, the theory, model and algorithm of the process optimization power flow are studied on the basis of the proposed linear time period, the approximate linear time variability of the node voltage, and the goal of greatly simplifying the problem. Finally, through simulation analysis, comparing the results of model 1 and model 2, we can see that from the point of view of the power abandonment cost, the power abandonment cost of model 1 is 2.29 million yuan, which is less than the 8 million yuan of model 2. From the perspective of depth peaking and start-stop times, the total depth peaking of model 1 is 331 times, which is greater than that of model 2, 258 times. The total start-stop of model 1 is 15 times, which is more than 9 times of model 2, so the total cost of optimized electricity of wind and light in model 2 is less than that in model 1. According to the optimal scheduling model of wind and light renewable energy, the effective utilization rate of renewable energy in China is low, and the teaching system should pay attention to this problem. Therefore, this paper puts forward an international English teaching strategy for the utilization rate of renewable energy.

Distributionally robust planning for power distribution network considering multi-energy station enabled integrated demand response

—To copy with the peak valley difference and peak load problem of power distribution network (PDN), this paper proposes a distributionally robust planning model for PDN considering the flexible management via the multi-energy station (MES) based integrated demand response (IDR). The planning covers reinforcement of the existing substation and feeders as well as the investment of MES and IDR. The IDR involves the incentive interruptible load response (ILR), transferable load response (TLR) and price-based power-gas substitution (PGS) which is implemented on MES. The MES integrates multiple energy storages and energy coupling devices, which is served as a platform for PDN operator to centrally track the final demand and improve the response potential of IDR. The proposed planning method aims at minimizing the present value of PDN investment cost and operation cost during the planning period. To handle the uncertainty of PDN end-use loads, a two-stage Kullback–Leibler divergence (KLD)-based distributionally robust optimization (DRO) planning model is formulated. Finally, the columns and constraints generation algorithm is utilized to solve the KLD-DRO planning model. Case studies are carried out on a practical 152-bus urban distribution system to validate the effectiveness of the proposed methodology and quantitatively analyze the key influence factors of PDN investment.

Consequence simulation of cyber attacks on key smart grid business cases

The increasing threat of cyber-attacks on modern power systems highlights the need for a comprehensive examination through simulations. This study conducts an in-depth simulation of cyber-attacks on critical smart grid components, including smart meters, substation automation, and battery management systems, to expose and analyze potential disruptions to power system operations. We identify vulnerabilities that can lead to severe grid instabilities, such as voltage variations, system collapses, and inverter failures. Our analysis underscores the complex interactions between cyber threats and grid components, revealing how disruptions extend beyond mere load interruptions to affect the core infrastructure. We advocate for integrating established cybersecurity frameworks like NIST, ISO/IEC 27001, and IEC 62443, essential in fortifying grid stability against these dynamic threats. Our findings highlight the urgent need for continuous adaptation and enforcement of these frameworks to enhance resilience and ensure the reliability of modern power grids against cyber-attacks.

Distributed optimal scheduling for virtual power plant with high penetration of renewable energy

The virtual power plants are aggregations of distributed generators, grid-connected devices in user side. The operation of virtual power plants affects the economic benefits and environmental benefits. However, due to the stochastic and fluctuating nature of power generation from renewable energy sources, the optimal scheduling problem for the virtual power plant containing high-dimensional random variables is difficult to solve. The conic power flow constraints are considered in the virtual power plant day-ahead optimal scheduling models to maximize the revenue by virtue of optimizing the flexible resources such as energy storage system and interruptible load. To quantify the uncertainties in the optimization problem, this paper proposes a network state-based power scenario reduction strategy for renewable energy generation, where typical scenarios are selected by the state of the grid voltage. The proposed day-ahead scheduling model is a mixed-integer, nonlinear, large-scale, stochastic optimization problem with high dimensional random variables, which is difficult to be solved directly by traditional centralized method. By temporally decoupling the charging and discharging model of energy storage systems, a distributed optimization algorithm is proposed based on multi-temporal power flow decoupling optimization model. The simulation results show that the proposed distributed optimization algorithm has high accuracy and good convergence, the virtual power plant can achieve day-ahead optimal scheduling and effectively promote renewable energy accommodation under the security and reliability of the grid.

Alternating direction method of multipliers based distributed energy scheduling of grid connected microgrid by considering the demand response

Global warming, environmental degradation, clean energy production, intermittent, volatile, and unpredictable renewable energy sources (RES’s), occasional peak demand on the system necessitates energy management (EM). Demand response (DR) programs in the distribution network can be seen as one of the foundation stones in the future of EM. This article illustrates the need for EM using DR, its benefits, types of loads, clustering techniques, price-based demand response (PBDR) etc. To accomplish the EM goals and to attain the economic benefit, DR employs peak shifting, peak clipping, valley filling and load growth. However, the accumulation of large loads at low electricity prices creates local peaks, this phenomenon is referred to as payback or rebound effect (RE). The occurrence of RE at low price zone heightens the volatility of market clearing price (MCP) and the operational cost of the microgrid. Inherently, the scheduled inelastic consumers at low price zone suffer from increased MCP and therefore, the total consumer tariff (TCT). The occurrence of RE depends on the load curve, peak to average ratio, electricity price and the percentage of interruptible loads present in the system. Unclear pricing methods impede the participation of customers in DR events. Moreover, majority of techniques presented in literature are of centralized frameworks that needs complex communication technologies. To fill these glitches the proposed work uses a simple distributed scheduling approach based on alternating direction method of multipliers (ADMM) to alleviate the energy management using an IEEE-18 bus system. The load factor increases from 0.79 to 0.83. Using DR lowers the peak power demand on the MG from 82 to 78 kW without compromising customer comfort or satisfaction. The TCT was lowered from scenario 1 to scenario 4 from 3058 to 2254 euros. The system's average demand dropped from 65.54 kW to 64.8 kW. IEEE-33 bus system was considered to assess the impact of RE on the MCP and TCT. Additionally, the marginal generator provides 72.51 kW of electricity in sub case 3 and 166.26 kW of power in sub case 2. Due to a decrease in power dispatch from the marginal generator, TCT increased from sub case 2 to sun case 3 by 11,046.41 rupees to 12,912.75 rupees. In contrast, TOC decreased from 6495.45 rupees to 6150.75 rupees from sub case 2 to sun case 3.

A Blocking Method for Overload-Dominant Cascading Failures in Power Grid Based on Source and Load Collaborative Regulation

Adjusting generator output and cutting off load can effectively solve the problem of continuous overload and disconnection of power lines caused by power flow transfer. To suppress large-scale power outages caused by overload-dominant cascading failures, a blocking method for overload-dominant cascading failures in the power grid based on source and load collaborative regulation is proposed. The shortest path algorithm was used to identify loads and generators with shorter electrical distances from overloaded lines. Under the premise of meeting the static safety constraints of the power grid, considering the regulation of generator output and the removal of interruptible loads, a multiobjective cascading failure blocking model is established with the minimum overall control cost of the system and the minimum probability coefficient of line failure. Use the NSGA-II algorithm to solve the model and obtain the optimal plan for adjusting generator output and cutting off load. Through example verification, the proposed method can effectively alleviate the phenomenon of line overload, thereby blocking the continuation of overload-dominant cascading failures.

Energy optimization of the smart residential electrical grid considering demand management approaches

-Many researchers have investigated the optimal energy scheduling to meet the demand regarding sustainable development issues and the economic and technical indices. This study presents the day-ahead operation of a smart residential distribution electrical grid as a bi-stage-multiple-criteria decision-making modeling. The proposed energy optimization is implemented based on optimal demand management in upper-stage and multiple-criteria decision-making in lower-stage. The multiple-criteria problem is modeled from the viewpoint of the grid's operator to optimize energy consumption costs, power losses and demand side comfort. The optimal demand management in upper-stage is coordinated considering price traffic in the upstream grid. The load shifting approach and load interruption approach are presented as optimal demand management for residential consumers. The operation of demand management by using load shifting and load interruption approaches is done via deferrable loads and clippable loads in smart residential homes, respectively. The proposed energy optimization in both stages by improved sunflower optimization (IFSO) is handled, and the TOPSIS method is proposed for the best trade-off of the multiple-criteria decision-making. The day-ahead energy operation is applied on the 33-bus distribution network to investigate the effectiveness of the residential distribution electrical grid considering obtained results from mathematical modeling and demand management approaches.

Construction of Auxiliary Service Transaction Model of Power Reserve for Interruptible Load

To ensure the safe and stable operation of the power system in the power market environment, a transaction model of power reserve ancillary services oriented to interruptible loads is constructed. By analyzing the benefits of interruptible users and power grid enterprises, a transaction model of interruptible load is established based on it. After obtaining the value of the minimum cost of power system to purchase interruptible load, the capacity cost, opportunity cost and standby use cost of power reserve are calculated respectively. The objective function of auxiliary power reserve service transaction model with interruptible load is established and the constraint conditions are set, and then the transaction result of auxiliary power reserve service is obtained by solving the objective function of the model. The experimental results show that the model can effectively calculate the power interruption compensation price of user interruptible load, and obtain the interruptible load transaction results according to the rapid capacity quotation, power quotation, etc. At the same time, the model has the lowest cost of reserve capacity and reserve use cost in a certain period of time, and the application effect is good.

An innovative method for building electricity energy management in smart homes based on electric vehicle energy capacity

The surging demand for electricity, fueled by environmental concerns, economic considerations, and the integration of distributed energy resources, underscores the need for innovative approaches to smart home energy management. This research introduces a novel optimization algorithm that leverages electric vehicles (EVs) as integral components, addressing the intricate dynamics of household load management. The study’s significance lies in optimizing energy consumption, reducing costs, and enhancing power grid reliability. Three distinct modes of smart home load management are investigated, ranging from no household load management to load outages, with a focus on the time-of-use (ToU) tariff impact, inclining block rate (IBR) pricing, and the combined effect of ToU and IBR on load management outcomes. The algorithm, a multi-objective approach, minimizes the peak demand and optimizes cost factors, resulting in a 7.9% reduction in integrated payment costs. Notably, EVs play a pivotal role in load planning, showcasing a 16.4% reduction in peak loads and a 7.9% decrease in payment expenses. Numerical results affirm the algorithm’s adaptability, even under load interruptions, preventing excessive increases in paid costs. Incorporating dynamic pricing structures like inclining block rates alongside the time of use reveals a 7.9% reduction in payment costs and a 16.4% decrease in peak loads. In conclusion, this research provides a robust optimization framework for smart home energy management, demonstrating economic benefits, peak load reduction potential, and enhanced reliability through strategic EV integration and dynamic pricing.

Optimization model of new energy consumption based on load demand-side response

As a technically and economically feasible and potentially resource-rich regulation tool, demand-side response can effectively strengthen the guarantee of new energy consumption, and is expected to play an enhanced key role in the construction of new power systems. This paper analyzes the factors affecting the linkage between demand-side and new energy generation side, the promotion of new energy consumption by demand-side response, and proposes a method to respond to the change of new energy output from the customer side, using interruptible load, transferable load, shiftable load, and curtailable load strategies to respond to the peak regulation problem brought by new energy access to the grid. The optimal demand-side response strategy is obtained by constructing a new energy consumption optimization model and solving it with the direct non-inferiority solution method, taking into account the economic and new energy consumption objectives. Finally, the effectiveness of the method is verified by simulating the IEEE 37-node system with multiple new energy configurations.

Co-Optimization Strategy of Island Distribution Grid and Time-of-Use Pricing Considering the Time Response Characteristics of Multiple Interruptible Loads

Co-Optimization Strategy of Island Distribution Grid and Time-of-Use Pricing Considering the Time Response Characteristics of Multiple Interruptible Loads

A bi-level economic evaluation of interruptible loads for daily energy acquisition of a distribution company

A bi-level economic evaluation of interruptible loads for daily energy acquisition of a distribution company

Optimal Electricity Load Interruption Based on Time Series Classification With Super Learner and Feature Filtering

Optimal Electricity Load Interruption Based on Time Series Classification With Super Learner and Feature Filtering

Low-Carbon economic scheduling with Demand-Side response uncertainty in regional integrated energy system

Effective mitigation and reduction of carbon emissions is consistently underscored in Regional Integrated Energy Systems (RIES), aligning with the goals of carbon-neutralization. However, the inherent trade-off between carbon emissions reduction and economic operation is an obvious challenge not only in power systems but especially in the coupled electricity-gas-heat energy systems. This paper develops a bi-level low-carbon optimal scheduling model, highlighting reliable operation and the balance of supply and demand of RIESs with demand-response uncertainty. In this paper we firstly redefined the carbon emission of a RIES as a carbon flow and proposed a reward and punishment carbon trading mechanism based on the ladder carbon price. Secondly, we designed an incentive-based demand-side response policy within the subsystem encompassing electricity, gas, and heat. Concurrently, the probability density distribution functions of transferable, replaceable, and interruptible loads are computed separately. Based on the above, the deviation of the load reduction and the actual load is recalculated to delimit the constraint of the demand-side response. Finally, the mechanism of carbon trading and the demand-response is leveraged to address the economic scheduling incorporating operation cost, carbon transaction cost, imbalance costs of supply and demand, and load loss. Simulations demonstrated in the paper prove that the optimal solution with demand-response uncertain may effectively increase the economic performance and reduce carbon emissions of a RIES.

A multi-layer scheduling framework for transmission network overload alleviation considering capabilities of active distribution networks

As distributed generations and flexible loads are widely connected to distribution networks, traditional distribution networks become “active”, and the response of the active distribution network (ADN) to the transmission network is receiving increasing attention. In this paper, the congestion problem of transmission network is studied from a demand-side perspective, and a multi-layer scheduling framework considering capabilities of ADNs is developed to reduce unnecessary load shedding operations in transmission network. The proposed framework consists of three layers. In the first layer, ADN’s power flow is linearized by the second-order cone programming, based on which a reactive power optimization scheduling model is constructed to minimize network loss in ADNs. When the network loss reduction in ADN cannot meet the requirements for congestion mitigation, a bi-level self-cycle model based on analytical target cascading is constructed in the second layer, which contributes to dispatch distributed generators’ outputs and further curtail load from the ADN side. When the second layer fails to satisfy the required load curtailment, a demand response model considering user acceptance and rejection is proposed to obtain the optimal shedding strategy for interruptible loads, and ultimately alleviate the overload problem. A case study with varying degrees of overload in the transmission network is conducted to demonstrate the proposed framework.

Multi-Type Reserve Collaborative Optimization for Gas-Power System Constrained Unit Commitment to Enhance Operational Flexibility

With the wide application of the gas-power system, gas-power coupling equipment such as gas turbines are gradually becoming widely used, and the problem of insufficient system reserve capacity needs to be solved. In order to improve the operational flexibility of the gas-power system, this paper combines the source side, the load side, and the energy storage side to propose a multi-type backup system, and constructs the source-load-storage multiple reserve capacity system of the gas-power system. Through the participation of gas turbine, steam turbine, interruptible load, and energy storage battery to provide reserve capacity, it can fully cope with the output fluctuation of the load side and source side and realize the coordinated operation of multiple resources to provide reserve capacity. In addition, the coordination of gas turbines and steam turbines can further improve the operation flexibility of the gas-power system. Through the example analysis, it was found that the proposed method reduced the total operating cost of the system by 10.6%.

Technical and economic operation of VPPs based on competitive bi–level negotiations

In recent years, because of the shortage of fossil fuels and their consequent price increase, along with the environmental pollution associated with these types of fuel, the use of renewable energy resources has increased considerably. Moreover, regarding the technical and economic benefits of the use of distributed energy resources (DERs), these resources play an important role in the electricity market. Virtual power plants (VPPs) are decentralized energy management systems that participate as independent units in the electricity market by collecting the capacity of distributed energy resources, including distributed generation units, storages, and interruptible loads. In this paper, the VPPs collect DERs and use them to participate in the power retail market and assist in meeting the forecasted demand of the distribution network. The main goal is optimal pricing of produced energy of VPPs for long-term Bi–level negotiations with the utility. To reach the goal, two different conditions are considered. In the first mode, there is no competition among VPPs over offering price to utility and cooperation in providing load. In this mode, the total benefit of VPPs is optimized, and they offer their prices to utilities based on energy market prices in upstream networks. In the second state, the competition among VPPs is modeled by using game theory. Then, the optimization is done separately for all VPPs, and the criterion for offering the price for each VPP is the energy market price in upstream networks and also the suggested prices of neighboring VPPs. In both modes, the utility, as the only owner and operator of the distribution network, evaluates the suggested energy price of VPPs with the benefit obtained from the dispatch of these power plants and decides on the amount and the time that it must dispatch from distributed generation units of these power plants. The results show that, by applying the proposed method in the competition mode, the profit of both utility and VPPs has enhanced.

Future of sustainable renewable-based energy systems in smart city industry: Interruptible load scheduling perspective

As the industrial and household loads continue to grow in smart cities, power supply and usage balance is getting more challenging. The increase in supply-side energy production is one method of alleviating peak demand proposed by a few investigations. As peak loads last for a short time, additional installations might be required. In addition, facilities that store energy can be used to reduce peak demand. As a consequence of the previous investigation, interruptible load regulation was not taken into account in optimizing system performance. The regulation of interruptible loads can greatly reduce system peak loads in smart city and within the concept of microgrids (MGs). The study proposes an interruptible load scheduling (ILS) scheme that considers consumer subsidy rates with the goal of reducing the peak load of the MG and its operating prices. To this end, a digital twin is developed in which the consumer interruption load time and minimal per-day load decrease restrictions have been completely met in the scheme. The ILS model is solved using a combination of the Min-Conflict Algorithm and the Gray Wolf Optimization Algorithm. Furthermore, simulations demonstrate the efficiency of the suggested algorithm and the show the substantial benefits of the scheme in reducing peak loads and operating prices in smart cities.