Two-stage Optimal Scheduling Research Articles

Two-Stage Optimal Scheduling Strategy of Microgrid Distribution Network Considering Multi-Source Agricultural Load Aggregation

With the proposed “double carbon” target for the power system, large-scale distributed energy access poses a major challenge to the way the distribution grid operates. The rural distribution network (DN) will transform into a new local power system primarily driven by distributed renewable energy sources and energy storage, while also being interconnected with the larger power grid. The development of the rural DN will heavily rely on the construction and efficient planning of the microgrid (MG) within the agricultural park. Based on this, this paper proposes a two-stage optimal scheduling model and solution strategy for the microgrid distribution network with multi-source agricultural load aggregation. First, in the first stage, considering the flexible agricultural load and the market time-of-use electricity price, the economic optimization is realized by optimizing the operation of the schedulable resources of the park. The linear model in this stage is solved by the Lingo algorithm with fast solution speed and high accuracy. In the second stage, the power interaction between the MG and the DN in the agricultural park is considered. By optimising the output of the reactive power compensation device, the operating state of the DN is optimised. At this stage, the non-linear and convex optimization problems are solved by the particle swarm optimization algorithm. Finally, the example analysis shows that the proposed method can effectively improve the feasible region of safe operation of the distribution network in rural areas and improve the operating income of a multi-source agricultural load aggregation agricultural park.

Two-Stage Optimal Scheduling for Urban Snow-Shaped Distribution Network Based on Coordination of Source-Network-Load-Storage

With the widespread integration of distributed resources, optimizing the operation of urban distribution networks faces challenges including uneven source-load-storage distribution, fluctuating feeder power flows, load imbalances, and network congestion. The urban snow-shaped distribution network (SDN), characterized by numerous intra-station and inter-station tie switches, serves as a robust framework to intelligently address these issues. This study focuses on enhancing the safe and efficient operation of SDNs through a two-phase optimal scheduling model that coordinates source-network-load-storage. In the day-ahead scheduling phase, an optimization model is formulated to minimize operational costs and mitigate load imbalances. This model integrates network reconfiguration, energy storage systems (ESSs), and flexible load (FL). During intra-day scheduling, a rolling optimization model based on model predictive control adjusts operations using the day-ahead plan to minimize the costs and penalties associated with power adjustments. It provides precise control over ESS and FL outputs, promptly correcting deviations caused by prediction errors. Finally, the proposed model is verified by an actual example of a snow-shaped distribution network in Tianjin. The results indicate significant improvements in leveraging coordinated interactions among source-network-load-storage, effectively reducing spatial-temporal load imbalances within feeder clusters and minimizing the impact of prediction inaccuracies.

Optimization Strategies for Electric Vehicle Charging and Routing: A Comprehensive Review

Based on information from recent research, by 2045, Electric Vehicles (EV) will dominate the roads with presence of more than 80% of its kind. Hence, these vehicles' grid level penetration will increase proportionally, which challenges the existing grid infrastructure in terms of its reliability and energy management capabilities. New techniques to store and consume massive quantities of energy from the power grid, as well as infusing the captive energy within the EV in response to grid demands, are emerging with the advent of electric vehicles. Everything could be handled smoothly only if we schedule the EV operation (charging/discharging) more optimally and efficiently using scheduling algorithms. Despite the existence of many routings and charging schedule computations, nature-inspired optimization approaches might play a critical role in responding to such routing challenges. Researchers have created several optimum scheduling approaches, such as Dynamic Programming, Differential Evolutionary Optimization Techniques, Collaborative Optimization Scheduling, Two-stage optimal scheduling strategy, and so on. The optimum schedule review examines the operation of an EV fleet while considering uncertainty sources and varied EV operating circumstances by integrating heuristic and meta-heuristic techniques. This paper exhibits a deep review on the various EV optimal scheduling techniques and adopted algorithms which are the emerging best practices like predictive analytics, dynamic routing, user centric planning, multi-objective optimization, etc. that reflect the industry's focus on leveraging advanced technologies, data-driven decision-making, and collaborative approaches to enhance the efficiency and sustainability of electric vehicle routing and charging scheduling.

Two-Stage Optimal Scheduling Based on the Meteorological Prediction of a Wind–Solar-Energy Storage System with Demand Response

With large-scale wind and solar power connected to the power grid, the randomness and volatility of its output have an increasingly serious adverse impact on power grid dispatching. Aiming at the system peak shaving problem caused by regional large-scale wind power photovoltaic grid connection, a new two-stage optimal scheduling model of wind solar energy storage system considering demand response is proposed. There is a need to comprehensively consider the power generation cost of various types of power sources, day-ahead load forecasting information, and other factors and plan the day-ahead output plan of the energy storage system with the minimum system operation cost as the optimization objective of day-ahead dispatching. The demand response strategy is introduced into the time-ahead optimal scheduling, and the optimization of the output value of the energy storage system in each period is studied with the goal of minimizing the system adjustment cost. The particle swarm optimization algorithm is used to solve the model, and the IEEE33 node system is used for an example simulation. The results show that using the demand response and the collaborative effect of the energy storage system can suppress the uncertainty of wind power and photovoltaic power, improve the utilization rate of the system, reduce the power generation cost of the system, and achieve significant comprehensive benefits.

A two-stage optimal scheduling strategy of hybrid energy system integrated day-ahead electricity market

A two-stage optimal scheduling strategy of hybrid energy system integrated day-ahead electricity market

Two-Stage Optimal Scheduling of Community Integrated Energy System

Two-Stage Optimal Scheduling of Community Integrated Energy System

A Deep Neural Network-Based Optimal Scheduling Decision-Making Method for Microgrids

With the rapid growth in the proportion of renewable energy access and the structural complexity of distributed energy systems, traditional microgrid (MG) scheduling methods that rely on mathematical optimization models and expert experience are facing significant challenges. Therefore, it is essential to present a novel scheduling technique with high intelligence and fast decision-making capacity to realize MGs’ automatic operation and regulation. This paper proposes an optimal scheduling decision-making method for MGs based on deep neural networks (DNN). Firstly, a typical mathematical scheduling model used for MG operation is introduced, and the limitations of current methods are analyzed. Then, a two-stage optimal scheduling framework comprising day-ahead and intra-day stages is presented. The day-ahead part is solved by mixed integer linear programming (MILP), and the intra-day part uses a convolutional neural network (CNN)—bidirectional long short-term memory (Bi LSTM) for high-speed rolling decision making, with the outputs adjusted by a power correction balance algorithm. Finally, the validity of the model and algorithm of this paper are verified by arithmetic case analysis.

Bilevel Optimal Scheduling of Electric Bus Fleets in Regional Integrated Electricity–Gas–Heat Energy Systems

Proliferation of electric buses (EBs) is widely acknowledged as a viable solution to reduce carbon emissions while addressing the demands of ground transportation. This paper investigates how to optimally schedule EBs in regions where heterogeneous energy resources, such as electricity, gas, and heat, are integrated as a regional integrated energy system (RIES). A novel bi-level optimal scheduling framework considering EBs’ operation schedules and vehicle-to-grid (V2G) responses is proposed in this work. Firstly, instead of charging-by-need (e.g., low SOC), this paper considers charging-by-scheduling (e.g., low electricity price) to optimize regional energy cost by explicitly incorporating coupled constraints from EB operation schedules, renewable energy generations, gas network models, and heat supply demands. The upper level scheduling strategy is formed to minimize the operation cost of the entire RIES. Secondly, EB operation strategies and energy generation & heat conversion devices (e.g., CHP, electric boilers, and gas boilers) are further optimized in the lower level to minimize fluctuation in the total electrical load. Finally, numerical simulation results of coordinated scheduling of EB charging, renewable integration, and gas supply to meet electrical and heat demands under different scenarios are compared and analyzed. Simulation results based on five scenarios show that the bi-level model and the two-stage optimal scheduling method can realize the optimal operation cost and reduce the electrical load fluctuation of the system.

Two-Stage Optimal Scheduling of Highway Self-Consistent Energy System in Western China

Under the background of “carbon peaking and carbon neutrality goals” in China, the Highway Self-Consistent Energy System (HSCES) with renewable energy as the main body has become a key research object. To study the operational status of the HSCES in a specific region and realize the economically optimal operation of the HSCES, an HSCES model in a low-load, abundant-renewable-energy and no-grid scenario is established, and a two-stage optimal scheduling method for the HSCES is proposed. Moreover, in the day-ahead stage, uncertainty optimization scenarios are generated by Latin hypercube sampling, and a definition of the self-consistent coefficient is proposed, which is used as one of the constraints to establish a day-ahead economic optimal scheduling model. Through the case comparison analysis, the validity of the day-ahead scheduling model is confirmed and the optimal day-ahead scheduling plan is attained. Furthermore, in the intra-day stage, an intra-day rolling optimization method is proposed, which can effectively track the day-ahead scheduling plan and reduce the impact of forecast errors and energy fluctuations by coordinating the unit output within the HSCES system. It is verified that the HSCES can operate economically and safely in Western China, and self-consistently, without grid support.

Minimizing maximum lateness in two-stage projects by tropical optimization

We are considering a two-stage optimal scheduling problem, which involves two similar projects with the same starting times for workers and the same deadlines for tasks. It is required that the starting times for workers and deadlines for tasks should be optimal for the first-stage project and, under this condition, also for the second-stage project. Optimality is measured with respect to the maximal lateness (or maximal delay) of tasks, which has to be minimized. We represent this problem as a problem of tropical pseudoquadratic optimization and show how the existing methods of tropical optimization and tropical linear algebra yield a full and explicit solution for this problem.

Risk-based Two-stage Optimal Scheduling of Energy Storage System with Second-life Battery Units

Risk-based Two-stage Optimal Scheduling of Energy Storage System with Second-life Battery Units

A two-stage IGDT-stochastic model for optimal scheduling of energy communities with intelligent parking lots

The proliferation of green mobility will bring multiple benefits to the society; however, it may be counterproductive for power systems if its integration is not properly planned. In this context, Intelligent Parking Lots have emerged as a valuable paradigm for integration of electric vehicles into energy systems. This framework consists of a set of vehicles that are managed as a whole and makes possible to exploit them as large storage facilities through their vehicle-to-grid capability. This particular feature may be significantly advantageous for energy communities since they can exploit parking lots as collective storage systems. In this paper, a two-stage optimal scheduling framework has been developed for optimal scheduling of energy communities. The proposal uses a stochastic representation of the state-of-charge of the lots with the end of accounting for random behaviour of uncertainties. On the other hand, the uncertainty of the upstream energy market is dealt with Information Gap Decision theory, resulting in an original hybridization that allows to adopt a risk-averse strategy by the operator. The optimization problem is formulated as a Mixed-Integer Linear programming model that can be efficiently solved by average solvers. A case study is performed to validate the new proposal and analyse the role of Intelligent Parking Lots in energy communities. The results evidence the advantages that electric vehicles may bring to communities if they are optimally exploited, highlighting their capability to enhance the efficiency and economy of the system.

A two-stage optimal scheduling model of integrated energy system based on CVaR theory implementing integrated demand response

A day-ahead and real-time two-stage risk economic optimal model of integrated energy system (IES) is established. First, considering the electricity and heating coupling characteristics of combined heat and power, the feasible region is described by mathematical model, and the integrated demand response model is expanded from the traditional demand response model. Second, the objective functions and constraints of two stages are established respectively. The first stage optimal objective is to minimize the pre-scheduled operation cost of day-ahead, which arranges the output power of renewable energy and the startup-shutdown plan, output power and reserve capacity of adjustable equipment. The second stage optimal objective is to minimize the re-scheduled expected cost of real-time, which will call reserve capacity, curtail renewable energy output, implement integrated demand response, and use energy storage to cope with power deviations. In order to quantify the risk cost of multiple uncertainties of power, load, and price, the real-time stage objective function is further improved to a form of conditional value at risk. Finally, simulations implemented on a green park show that: the proposed model can achieve the optimization of energy supply at different time scales and improve scheduling enforceability after considering economics and risk. Shapely Value can fairly and reasonably determines the benefit distribution scheme of different subjects in IES.

A two-stage optimal scheduling method for combined heat and power systems considering thermal inertia

A two-stage optimal scheduling method for combined heat and power systems considering thermal inertia

Two-stage optimal scheduling of virtual power plant with wind-photovoltaic-hydro-storage considering flexible load reserve

In view of the current situation of China’s power system to make regulation for power generation and load side balance mainly by power plants that are subject to unified scheduling, the existing non-uniformly scheduled small hydropower resources of China can be integrated and utilized effectively when considering the aggregation and control of virtual power plant (VPP) towards distributed resources. Meanwhile, the economic operation of VPP under different seasons of rivers can be achieved, and the consumption of renewable energy such as wind power and photovoltaic (PV) can be promoted. In addition, a certain flexible load reserve (FLR) capacity can be obtained by signing medium and long-term contracts. In this context, a day-ahead and real-time two-stage rolling optimal scheduling strategy for VPP including distributed wind power, PV, and small hydropower is proposed. In the day-ahead stage, aiming at the minimization of operation cost and adopting the multi-scenarios analysis method, the day-ahead scheduling and FLR plan of VPP is formulated. In the real-time stage, with preciser wind and PV prediction, the economic scheduling considering deviation penalty cost can be achieved. The case study of VPP in Jingning County, Lishui City, Zhejiang Province of China verifies the effectiveness and practicability of the proposed method.

Study on the Optimal Dispatching Strategy of a Combined Cooling, Heating and Electric Power System Based on Demand Response

This paper proposes a combined cooling, heating and electric power (CCHP) system based on demand side response. In order to improve the economy of the system, a two-stage optimal scheduling scheme is proposed with the goal of minimizing the total operating cost of the system and maximizing user satisfaction. The optimal operation of the system was divided into two optimization problems, including the demand side and the supply side. In the first stage, combined with user satisfaction, from the new point of view that users are prone to excessive behavior due to time-of-use electricity prices, the cooling, heating and power load curves are optimized. In the second stage, an economic dispatch model that includes operating costs in terms of energy, maintenance and environment is established. An improved artificial bee colony (IABC) algorithm is used to solve the optimal energy production scheme based on the demand curves optimized in the first stage. Case studies are conducted to verify the efficiency of the proposed method. Compared with the CCHP system that does not consider demand response, this method reduced operation cost on typical days in summer and winter by 5.20% and 5.76%, respectively.

A Two-Stage Optimal Dispatching Model for Micro Energy Grid Considering the Dual Goals of Economy and Environmental Protection under CVaR

With the development of distributed renewable energy, a micro-energy grid (MEG) is an important way to solve the problem of energy supply in the future. A two-stage optimal scheduling model considering economy and environmental protection is proposed to solve the problem of optimal scheduling of micro-energy grid with high proportion of renewable energy system (RES) and multiple energy storage systems (ESS), in which the risk is measured by conditional value-at-risk (CVaR). The results show that (a) this model can realize the optimal power of various energy equipment, promote the consumption of renewable energy, and the optimal operating cost of the system is 34873 USD. (b) The dispatch of generating units is different under different risk coefficients λ, which leads to different dispatch cost and risk cost, and the two costs cannot be optimal at the same time. The risk coefficient λ shall be determined according to the degree of risk preference of the decision-maker. (c) The proposed optimal model could balance economic objectives and environmental objectives, and rationally control its pollutant emission level while pursuing the minimum operation costs. Therefore, the proposed model can not only reduce the operation cost based on the consideration of system carbon emissions but also provide decision-makers with decision-making support by measuring the risk.

A two-stage optimal scheduling method for active distribution networks considering uncertainty risk

The uncertainty of distributed energy generation and the uncertainty risk of system components failure are important means to ameliorate the economy and reliability of Active Distribution Network (ADN). In this paper, a two-stage optimal scheduling method for ADN considering uncertainty risk is proposed. Considering the uncertainty risk of renewable resources, load and electricity price, and the uncertainty risk of system component failure, the two-stage optimal dispatching process of ADN with energy storage system is investigated. Firstly, the first stage model is established to minimize the cost of operation of ADN under normal operation conditions, and then the second stage model is established to minimize the load reduction under emergency operation. Particle Swarm Optimization (PSO) algorithm is proposed for model optimization. The simulation outcomes on IEEE-33 bus system simulation demonstrated high effectiveness of the proposed method.

Two-Stage Joint Optimal Scheduling of a Distribution Network With Integrated Energy Systems

The coordinated operation of an integrated energy system (IES) and a distribution network is the inevitable development trend of the energy Internet of the future. The day-ahead optimal scheduling of the IES is an important way to improve new energy efficiency and the energy economy. When the IES and the distribution network exchange electrical energy, the voltage of the distribution network may be out of limit. This article presents a two-stage joint optimal scheduling method for a distribution network with IESs to improve the economy of the IESs and the safety of the distribution network. In the first stage, the user’s demand response and the electrical energy interaction between IESs are considered, and the schedulable potential of the systems is fully tapped. In the second stage, a bi-level scheduling model is adopted: the upper model takes the distribution network as the control object and reduces the power loss by adjusting the exchange power between the distribution network and the IESs. The lower model takes the IESs as the control objects and obtains the scheme with the lowest cost in each IES through multi-objective particle swarm optimization. Taking the IEEE 33-node distribution system as an example, simulation research is performed to show that the total network loss is 17.01% lower and the total cost is 5.36% lower than the method without two-stage optimal scheduling, which verifies the effectiveness of the proposed method.

Two-Stage Optimal Scheduling of Large-Scale Renewable Energy System Considering the Uncertainty of Generation and Load

With the development of smart grid and low-carbon electricity, a high proportion of renewable energy is connected to the grid. In addition, the peak-valley difference of system load increases, which makes the traditional grid scheduling method no longer suitable. Therefore, this paper proposes a two-stage low-carbon economic scheduling model considering the characteristics of wind, light, thermal power units, and demand response at different time scales. This model not only concerns the deep peak state of thermal power units under the condition of large-scale renewable energy, but also sets the uncertain models of PDR (Price-based Demand Response) virtual units and IDR (Incentive Demand Response) virtual units. Taking the system operation cost and carbon treatment cost as the target, the improved bat algorithm and 2PM (Two-point Estimation Method) are used to solve the problem. The introduction of climbing costs and low load operating costs can more truly reflect the increased cost of thermal power units. Meanwhile, the source-load interaction can weigh renewable energy limited costs and the increased costs of balancing volatility. The proposed method can be applied to optimal dispatch and safe operation analysis of the power grid with a high proportion of renewable energy. Compared with traditional methods, the total scheduling cost of the system can be reduced, and the rights and obligations of contributors to system operation can be guaranteed to the greatest extent.