Interruptible Load Research Articles

A Dispatching Method for Large-Scale Interruptible Load and Electric Vehicle Clusters to Alleviate Overload of Interface Power Flow

The study of dispatching methods for large-scale interruptible loads and electric vehicle clusters is of great significance as an optional method to alleviate the problem of overload in interface power flow. In this paper, the distribution model and transfer capacity of large-scale interruptible load and electric vehicle in two dimensions of time and space were firstly introduced. Then, a large-scale interruptible load and electric vehicle dispatching model considering transmission interface power flow balance was established. Finally, a case study was carried out with the city power grid as the research object. Studies show that by dispatching large-scale interruptible load and electric vehicle, the overload rate of interface power flow can be reduced by 12–17%, while the proportion of clean energy generation increased by 4.19%. Large-scale interruptible load and electric vehicles are quite different in terms of the role they play in grid regulation. The regulation cost of electric vehicles is higher than that of large-scale interruptible load, but it also has the advantages of promoting the consumption of clean energy and improving the overall operating economy. Which type of resource should be given priority is based on the actual state of the grid. In addition, the cost of electricity has a significant impact on the load response behavior of electric vehicles. It should be determined according to various factors, such as interface power flow control requirements, regulation costs, and power grid operation costs.

Hybrid active and passive strategies for chance-constrained bilevel scheduling of community multi-energy system considering demand-side management and consumer psychology

Developing hybrid renewable energy multi-energy system at the community level (CMES) is crucial for sustainable development and society. There are considerable resources in community users that can be activated by demand-side management (DSM) and combined with CMES for smart and clean energy supplies. This work focuses on optimal multi-timescale CMES management at energy and power levels considering source-load interaction. Firstly, the flexible community building and EV loads are comprehensively modeled, including the subjective psychological factors of community users' willingness to participate in DSM activities. Then, a bilevel collaborative strategy is proposed to manage CMES. The upper-level unites all schedulable resources to actively control CMES and flexible loads to achieve optimal multi-energy balances. The lower-level forms hybrid virtual electrical storage to passively adapt to instantaneous power rebound/decline events. These two levels are bridged through chance-constrained programming, achieving risk avoidance. Finally, the performances, including community users, the grid, and CMES itself, are evaluated, as well as influences of confidence level and user attitudes (in DSM activities). Simulation results show that the proposed strategy with a high confidence level avoids the lack of flexibility and load interruption and reduces cost and grid interaction fluctuations by 5.75 % and 59.48 %, respectively. DSM can support the efficient and flexible operation of CMES by providing equivalent energy storage with a capacity/power of up to 657 kWh/350 kW. On the other hand, the high subsidy price reduces CMES's interests, and users' benefits will be weakened accordingly, while users actively involved in DSM create a win-win situation with CMES.

Multi-type Adjustable Loads Aggregation Control Strategy for Industrial Parks

China’s industrial parks are the concentration areas of industrial enterprises. The aggregate regulation and control of various types of adjustable loads in industrial parks are effective means to achieve the consumption of renewable energy. However, there are many types of adjustable loads, along with varying amounts and response characteristics, which makes it difficult to achieve unified regulation. In view of the regulation and control characteristics of multiple types of adjustable loads in industrial parks, the regulation and control models of short-term interruptible load, reducible load, translatable load, transferable load and distributed energy storage are established respectively in this paper, aiming at minimizing the regulation cost and maximizing the benefits of the regulation and control auxiliary services, and the aggregate regulation and control strategy of multiple types of adjustable loads in industrial parks is proposed based on the electricity market environment to support the high proportion of renewable energy consumption of the new power system. At the same time, it will obtain auxiliary service income for industrial users. The simulation results of the example show that the proposed strategy reduces the power consumption cost of industrial users and effectively increases the usage of renewable energy without affecting the production of enterprises.

Wasserstein‐metric‐based distributionally robust optimization method for unit commitment considering wind turbine uncertainty

AbstractThe penetration of wind turbines in the power grid is increasing rapidly. Still, the wind turbine output power has uncertainty, leading to poor grid reliability, affecting the grid's dispatching plan, and increasing the total cost. Thus, a distributionally robust optimization method for thermal power unit commitment considering the uncertainty of wind power is proposed. For this method, energy storage and interruptible load are added to simulate increasingly complex electricity consumption scenarios. Furthermore, the amount of load cutting reflects the satisfaction level of electricity consumption on the user side. Based on Wasserstein metric, an ambiguity set is established to reflect the probabilistic distribution information of the wind power uncertainty. An ambiguity set preprocessing method is proposed to depict the probability distribution of ambiguity set more clearly, to minimize the operation cost under the condition that the uncertainty of wind turbine output power obeys the extreme probabilistic distribution of the ambiguity set. The test case in a modified version of the IEEE 6‐bus system shows that the proposed method can flexibly adjust the robustness and economy of optimization decisions by controlling the sample size and the confidence of Wasserstein ambiguity set radius. In addition, the proposed ambiguity set preprocessing method can obtain more economical dispatching decisions with a smaller sample size.

An optimal management architecture based on digital twin for smart solar-based islands incorporating deep learning and modified particle swarm optimization

Smart island (SI) energy management is a type of energy management system used to ensure that energy is used efficiently on islands. It is designed to reduce energy consumption and costs while also improving the sustainability of the island. Smart island energy management systems use a combination of technologies such as solar, wind, and other renewable energy sources, energy storage systems, smart meters, and advanced analytics to monitor and manage energy usage. The system can be used to provide islanders with real-time energy usage data, allowing them to make informed decisions about their energy use. Using the effects on the environment and economics of the SI as a basis for fully modeling the optimum performance of the SI in grid-connected operations, the feasibility and balance of the SI power grid are ensured. In addition, taking into account the impact of interruptible loads (IL) on SI operation costs, a power shift for IL can be performed using storage batteries in a digital twin environment. A new ecology-driven optimization algorithm has been developed that continuously adjusts migration rates with habitat suitability indexes of normalized individuals and adds a differential perturbation to the migration operator of the migration process. The enhanced particle swarm optimization algorithm has been implemented as the SI optimization dispatching algorithm. In order to have a precise prediction of the renewable energy sources, their output power is predicted using the recurrent neural network (RNN) deep learning model. Based on the simulation outcomes, it is evident that the suggested power dispatching model could greatly decrease the overall price of the system by implementing the advanced and effective algorithm and model presented in the study.

Market-based coordination of price-responsive demand using Dantzig-Wolfe decomposition method

The increasing penetration of Distributed Generation (DG) and Demand Responsive (DR) loads in power systems has necessitated the development of novel approaches to address the coordination problem of Price Responsive Devices (PRD). These PRDs are treated as self-interested players aiming to optimize their consumption patterns based on prevailing prices. In this paper, we propose a new algorithm based on the Dantzig-Wolfe (DW) Decomposition method, which tackles the coordination problem of self-interested PRDs in a distributed manner. Leveraging the distributed nature of the DW approach, we model the self-interested algorithms of PRDs as sub-problems within the DW framework. The coordinator, or grid operator, responsible for collecting the energy consumption information (energy bids) of PRDs, solves the master problem of the DW and determines the price signal accordingly.The proposed algorithm exhibits fast convergence as the sub-problems within DW, which could involve a large number of PRDs (potentially millions), can be solved simultaneously. Additionally, based on the DW theory, if the PRDs' subproblems are convex, reaching the optimal point (equivalent to Nash Equilibrium) is guaranteed within a limited number of iterations. To evaluate the proposed model, we conducted a simulation involving 200 participant households, each equipped with two types of loads: Electric Vehicles (EVs) as examples of interruptible loads, and Electric Water Heaters (EWHs) as examples of Thermostatically Controlled Loads (TCLs). The results demonstrate that when the algorithm converges to the optimal point, both the generation cost and user payment (based on the marginal cost of generation) decrease. Furthermore, there is a significant reduction in the Peak to Average Ratio (PAR) of the aggregate load

Coordinated active and reactive power management for enhancing PV hosting capacity in distribution networks

AbstractThis paper proposes an operational planning model based on optimal active and reactive power control strategies to enhance solar photovoltaics (PV)’ hosting capacity in distribution networks. Reactive power control is carried out through optimum static VAr compensators (SVCs) placement, while active power control is performed through flexible loads, particularly shiftable and interruptible loads. The first stage of the proposed two‐stage stochastic model assigns decision‐making regarding calculating PV hosting capacity at different nodes, in addition to the allocation and capacity of SVCs. In the second stage, the first stage decisions are assessed to ensure the power flow constraints under various uncertainties such as daily load and stochastic PV generation. The presented model is investigated through numerical analyses on modified IEEE 15‐bus and IEEE 33‐bus distribution systems considering different active‐reactive strategy cases. While most previous works only rely on one type of active or reactive power control strategy, this study investigates the challenges of the respective application of active and reactive power control in various modes of fundamental practices. The obtained results prove the superiority of the proposed hybrid active‐reactive control strategy for enhancing PV hosting capacity compared to respective active or reactive power controls.

A resilience-oriented optimal planning of energy storage systems in high renewable energy penetrated systems

Natural events having a low occurrence probability and high impacts, such as windstorms and earthquakes, pose a danger to the distribution networks' optimal performance. To increase network resiliency, several operational solutions are necessary. This paper shows how to restore prioritized loads while meeting topological and operational constraints using mixed-integer linear programming. The model presents a plan for enhancing the interconnection of renewable energy sources (RESs), stationary battery energy storage systems (SBESSs), and power electric vehicles parking lots (PEV-PLs), which are used in the distribution system (DS), to get the optimal planning under normal and resilient operation. The stochastic optimization technique is used to model the influence of upstream grid pricing uncertainty on the optimal scheduling of (DS). Other major uncertainties, such as wind power, photovoltaic (PV), and active/reactive power for different types of electrical loads, are also simulated using stochastic optimization. In addition, demand response programs (DRP) and interruptible loads are introduced to enhance the resilient operation of the DS. The suggested planning model is tested on the IEEE 33-bus benchmark test system. The results confirm the feasibility and effectiveness of the suggested model in normal and resilient operation and to assess its performance.

Coordinated optimization of source–storage–load in distribution network based on edge computation

A large number of distributed photovoltaics are linked to the distribution network, which may cause serious power quality problems. Based on edge computing, this article put forward a strategy that aggregates multiple distributed resources, such as distributed photovoltaics, energy storage, and controllable load to solve this problem, emphasizing the coordination and optimization between distributed resources. Firstly, an edge computing architecture that can be fully applicable to the coordination of source–storage–loads is constructed. Based on this architecture, the distributed photovoltaic, energy storage and interruptible loads are optimized with the minimum operating cost of edge computation nodes as the objective function. Combined with the operation requirements of the distribution system, lessen the system network loss and make sure the stable and safe operation of the net; Finally, the model is changed with a second-order cone model and verified in an improved IEEE 33 node example. The results show that this method can effectively reduce the loss cost of the system, and can effectively solve the problem of system voltage exceeding the limit, reduce the voltage fluctuation, and maintain the safe and stable operation of the system.

Flexible load optimal regulation strategy of commercial buildings under the environment of electricity market

Under the electricity market environment, the flexible load of power users can not only reduce the cost of power consumption by adjusting their own power consumption behaviors according to the real-time price (RTP), but also can obtain additional benefits by participating in auxiliary services. The load of commercial buildings (CBs) is concentrated and makes a greater contribution to the peak load of the power system. If the flexible load of CBs can be regulated, it has great benefits for shifting the peak load of the power system in addition to reducing the electricity cost. On the basis of analyzing the characteristics of flexible loads in CBs, a flexible load regulation model including short-time interruptible load (IL), reducible load (RL), translatable load (TL), air conditioning system (ACS) and energy storage system (ESS) is established. Considering the electrical comfort and temperature preference, the objective function is jointly constructed with the electrical cost, temperature discomfort and ancillary service income. An optimal regulation strategy for multiple types of flexible loads in CBs based on the electricity market environment to realize the economic power consumption of CBs is formulated. The simulation results show that the proposed strategy can reduce the power cost of CBs and lower the peak demand under the condition of satisfying the power consumption comfort.

Multiple Virtual Power Plants Transaction Matching Strategy Based on Alliance Blockchain

Virtual power plants can aggregate distributed energy resources and interruptible loads in a region for coordinated regulation and unified transaction. However, with the diversification of competition in the electricity market, the distributed operation mechanism between multiple virtual power plants (multi-VPPs) has gradually become a research focus. Based on this, this paper proposes a new type of distributed transactions strategy between multi-VPPs, i.e., the transaction matching mechanism. A two-stage transaction model based on the transaction matching is constructed for multi-VPPs to participate in the day-ahead and intraday electricity trading markets. In the first stage, each VPP optimizes its own internal units’ output and external interaction power through a cooperative game; in the second stage, it is the transaction matching among multi-VPPs that can match the most suitable counterpart by flexible price setting to increase the benefits of all the VPPs. Considering the efficiency and security of blockchain technology, we choose to complete the transaction matching between multi-VPPs with the support of alliance blockchain technology to improve the speed of system solution.

A research on family flexible load scheduling based on improved non-dominated sorting genetic algorithm

In demand response scheduling, residential electricity load has huge scheduling potential. However, the diversification of characteristics of residential electricity load, the diversity of user demands, and the poor convergence and susceptibility to local optimal solutions of scheduling optimization algorithms significantly increase the difficulty of demand response scheduling. To address the heterogeneity of the load, this paper proposes a new classification method for household electricity load, which is divided into rigid load and flexible load based on different electricity characteristics, and further divides flexible load into interruptible load, non-interruptible load, and reducible load, and establishes mathematical models for each type of load. To address the diversity of user demands, this paper proposes three optimization objectives: minimum electricity cost, minimum carbon emissions, and maximum user satisfaction, which can be comprehensively optimized based on user electricity demands. To address the slow convergence and susceptibility to local optimal solutions of the traditional NSGA-III algorithm, this paper improves the traditional NSGA-III algorithm by using adaptive crossover and mutation to improve algorithm convergence speed and selecting individuals based on crowding distance to avoid local optimal solutions. The results show that compared with the user's original electricity consumption behavior, this method can save up to 21.1% of electricity costs, reduce carbon emissions by up to 10.2%, improve user electricity satisfaction by up to 41.2%, and reduce peak-to-valley difference by up to 42.3%. This provides a powerful basis for residential buildings to participate in demand response scheduling and provides a reference path for achieving low-carbon development goals in the building field.

Micro-Grid Day-Ahead Stochastic Optimal Dispatch Considering Multiple Demand Response and Electric Vehicles

Multiple demand responses and electric vehicles are considered, and a micro-grid day-ahead dispatch optimization model with photovoltaic is constructed based on stochastic optimization theory. Firstly, an interruptible load model based on incentive-based demand response is introduced, and a demand response mechanism for air conditioning load is constructed to implement an optimal energy consumption curve control strategy for air conditioning units. Secondly, considering the travel demand and charging/discharging rules of electric vehicles, the electric vehicle optimization model is built. Further, a stochastic optimization model of micro-grid with demand response and electric vehicles is developed because of the uncertainty of photovoltaic power output. Finally, the simulation example verifies the effectiveness of the proposed model. The simulation results show that the proposed model can effectively tackle the uncertainty of photovoltaic, as well as reduce the operating cost of micro-grid. Therefore, the effective interaction between users and electric vehicles can be realized.

Low Carbon Operation Optimization Study of Integrated Energy Systems Considering Interruptible Loads

Aiming at the consumption of renewable energy, researched controllable load mechanisms and energy storage regulation strategies in an electric-thermal coupled network, and a comprehensive flexible resource model was constructed to optimize the low-carbon operation of the system. Firstly, analyzed the industrial load regulation mechanism of differentiated layout in the regional power grid. Based on the electric-thermal coupled system in the urban energy grid, the regulation model of controllable load and electric/thermal energy storage was established. Then a comprehensive benefit model was constructed with an economic cost, wind and solar usage, and carbon emissions as indicators. Proposed a carbon emission flow topology to describe the carbon emission flow information attached to the energy flow. Finally, combined with the IEEE 33-node distribution network and 45-node heat network coupling system, the improvement effect of the multiple flexible resource response on the comprehensive benefits of the system was analyzed. And the carbon emission flow process of the electricity-thermal network in the typical scenario was visually displayed through topology. The results show that exploring flexible resources can improve the consumption margin of wind and solar energy in the integrated energy system, which verifies the rationality and effectiveness of the proposed method.

A mixed integer linear programming model for risk‐based remote‐controlled switches, distributed generation, and tie line placement in distribution systems with complex topologies to improve the resilience

AbstractDue to the increasing global warming, it is anticipated that the number and severity of natural disasters will increase in the coming years. In this regard, this paper proposes a planning model to improve the resilience of distribution systems against natural disasters. A mathematical model is developed to determine the optimal locations of remote‐controlled switches (RCSs), distributed generation units (DGs), and tie lines in distribution systems with complex topologies or lateral branches. Simultaneous occurrence of multiple faults is considered to better simulate the extreme events. Moreover, the concept of multi‐microgrids is used to supply the maximum possible interrupted load after the fault occurrence. To manage the system risk and different failure scenarios, the conditional value at risk (CVaR) is added to the planning model. The optimization model has been formulated as a mixed integer linear programming (MILP) problem, which can be easily solved using various commercial solvers and give the global optimal solution. Finally, to illustrate the effectiveness of the proposed planning model on improving the distribution system resilience, it is implemented on the IEEE 33‐bus system using different case studies and sensitivity analyses.

Multi-Microgrids Operation With Interruptible Loads in Local Energy and Reserve Markets

In the evolution of the power systems, a particular case is the presence of a number of microgrids (MGs) operated with mutual interconnection, but without connection to the main distribution system. The interconnected MGs form a structure in which the overall system operation and resource scheduling can be determined by considering centralized or decentralized approaches. This article introduces local energy and reserve markets (LERMs) in which the MG managers (MGMs) can meet their required energy and reserve with optimal scheduling of their resources, besides competing with the other MGs. To model such decision-making framework for MGMs, a bilevel optimization approach is developed in which the MGMs’ problem is modeled as the upper level problem and the LERMs clearing problem is modeled as the lower level problem. This model is transformed into a mathematical programming with equilibrium constraints (MPEC) using the primal-dual transformation. Then, the resulting MPEC for each MG is replaced with its Karush–Kuhn–Tucker conditions, obtaining an equilibrium problem with equilibrium constraints (EPEC) model. The nonlinear terms of the model are linearized through different approaches. Finally, the EPEC model is transformed into a mixed-integer linear problem considering the objective function of all MGMs. The model is applied to a test system with three interconnected MGs. Moreover, the sensitivity of the results to the probability of calling reserve is investigated.

An MILP Model for Switch, DG, and Tie Line Placement to Improve Distribution Grid Reliability

Remote controlled switches (RCSs) have the ability to isolate the faulted area from other parts of the distribution system. On the other hand, the dispatchable distributed generators (DDGs) and tie lines can supply the interrupted loads after fault occurrence trough microgrids and reduce the outage time. In this regard, this article proposes a planning model for simultaneous placement of RCSs, DDGs, and tie lines to improve distribution system reliability. The presence of renewable distributed generations (RDGs) and energy storage systems, which have an increasing penetration in today's distribution networks are also considered. Moreover, two different practical load shedding methods are considered to balance the total generation and consumption in microgrids. The predetermined expansion plan of RDGs is also considered. To simplify the implementation of the proposed optimization model and ensure the global optimal solution, the planning model is formulated as a mixed integer linear programming model which can be solved by various commercial solvers. Finally, the effectiveness of the proposed model is illustrated by implementation on bus 4 of Roy Billinton test system through various case studies and sensitivity analyses. The results demonstrate the significant impact of the proposed planning model on improving the distribution system reliability.

Economic analysis of annual load loss due to voltage sags in industrial distribution networks with distributed PVs

In an industry, analysis of load disconnection indices is essential to determine the annual economic loss because of the high cost of load interruptions. With the interconnection of PVs in modern industrial networks, this analysis gets more complicated due to controls associated with PV inverters for reactive power support. Hence, the paper proposes a unique method for such analysis that relies on dynamic simulation to calculate load disconnections. The dynamic simulation allows the integration of the voltage tolerance curve of loads and controls associated with PVs making the scenarios more realistic. The indices are determined by employing statistical experiments which account for annual variability of daily solar and load profiles with the least possible runs of dynamic simulation. The proposed method is applied to calculate load disconnection indices for an IEEE 399-1997 industrial distribution system. A comparative analysis of the cost of disconnection under a variety of solar penetration and operation scenarios is presented. Network planners and reliability engineers can potentially utilize the proposed method to identify PV inverter controls, placement, and size effects on the annual load disconnection indices due to voltage sags.

Strategic bidding of gas-fired units with tanks in coupled electricity and natural gas markets

With the increasing interdependence between the electricity and natural gas systems in economy and physics, this paper addresses the problem of developing optimal offering strategies in synchronized electricity–gas markets with step-wise energy offer formats. The gas-fired generators act as price makers in electricity markets and interruptible loads in the natural gas markets. The proposed model considers not only the impact of electricity clearing prices on gas bidding behaviors but also the influence of fuel cost variation on developing optimal electricity bidding curves. A price update method is proposed with the help of gas tanks to suppress price fluctuation. A two-stage trading mechanism is designed for gas-fired generators to optimize bids in electricity and gas markets via an iterative method. These bilinear terms in objectives and constraints are tackled by primal–dual equality conditions and binary expansion method. Then, the mathematical program with equilibrium constraints is converted to the mixed-integer linear program. The diagonalization algorithm is nested in the proposed trading mechanism if there are many leaders in the market instead of just one. Case studies validate the proposed methodology in detail.

Does friction contribute to formability improvement using servo press?

Servo press forming machines are advanced forming systems that are capable of imparting interrupted punch motion, resulting in enhanced room temperature formability. The exact mechanism of the formability improvement is not yet established. The contribution of interrupted motion in the ductility improvement has been studied through stress relaxation phenomena in uniaxial tensile (UT) tests. However, the reason for improved formability observed when employing servo press is complicated due to the additional contribution from frictional effects. In the present work, an attempt is made to decouple the friction effect on formability improvement numerically. The improved formability is studied using a hole expansion test (HET). The limit of forming during hole expansion is modeled using the Hosford-Coulomb (HC) damage criteria, which is implemented as a user subroutine in a commercial explicit finite element (FE) software. Only the contribution of stress relaxation is accounted for in the evolution of the damage variable during interrupted loading. Therefore, the difference between simulation and experimental hole expansion ratio (HER) can be used to decouple the friction effect from the overall formability improvement during hole expansion. The improvement in HER due to stress relaxation and friction effect is different. The study showed that the model effectively captures the hole expansion deformation process in both monotonic and interrupted loading conditions. Compared to stress relaxation, friction effect played a major role during interrupted HET.