Optimal Day-ahead Scheduling Research Articles

Operation Management of Electric Vehicle Battery Swapping and Charging Systems: A Bilevel Optimization Approach

This paper studies optimal day-ahead scheduling of a battery swapping and charging system (BSCS) for electric vehicles (EVs) from a new perspective of multiple decision makers. It is considered that the BSCS locally incorporates the battery swapping and charging processes, and the two processes are managed by two operators, called a battery swapping operator (BSO) and a battery charging operator (BCO), respectively. Our main contribution is to propose a bilevel model where the BSO acts as the leader to receive and serve the battery swapping requests from EV users, and the BCO acts as the follower to interact with the grid and control battery charging and discharging power. We reformulate the bilevel optimization problem into an equivalent single-level problem that is a nonconvex mixed-integer nonlinear program (MINLP), and its size can easily become very large. To solve the problem efficiently, we develop a new heuristic composed of two parts, i.e., an estimation of the integer solution and an algorithm based on the alternating direction method (ADM). The results show that the proposed heuristic performs well in solving large-scale problems, providing close-to-optimal solutions quickly. In addition, compared to a social welfare maximization model that follows most existing related works, the proposed bilevel model can increase the number of swapped-out batteries by 35% and the batteries’ average energy state by 6%, improving the quality of battery swapping services.

A cooperative game approach for optimal resiliency-oriented scheduling of transactive multiple microgrids

The growth of distributed energy resources and microgrids (MGs) attracts attention to the transactive multiple MG (TMMG) concept. This paper proposes a novel framework that belongs to the competitive-based resilient scheduling of TMMGs. A cooperative game approach is investigated for optimal day-ahead scheduling of TMMGs, which concerns the possibility of long-duration disconnection from the main grid. The TMMG comprises multiple MGs that can have energy transactions through a local market. This local energy market lets the TMMG operator receive the MGs' 24-hour bid-offer amounts and set the hourly transaction prices. The proposed game fulfills the minimum operation cost for MGs in the normal operation mode (NOM) and ensures the serving of TMMG's load during the resiliency operation mode (ROM). Thus, the paper seeks to find an equilibrium point so that all the MGs reach acceptable strategies by meeting the constraints in the NOM and ROM. It is shown that the market tends to provide the NOM transaction at clearing prices lower than the wholesale price. Also, it tends to disperse a higher price in the ROM condition. Notwithstanding the profitable opportunity for renewable-penetrated MGs, they are exposed to high costs due to the inherent uncertainty and risk of generation deficiency.

Hybrid robust decentralized optimization of emission-aware multi-energy microgrids considering multiple uncertainties

Multi-energy microgrid (MEMG) can effectively coordinate various energy carriers and decarbonize the power system. Several MEMGs in the same distribution network are interconnected to form a multi-microgrid system, where a coordinator exists to coordinate these MEMGs to provide more cost-effective and reliable energy services. However, in this system, protecting each MEMG's information privacy is important. Moreover, the uncertainties caused by supply-demand sides and electricity price challenge the reliable operation of MEMGs. Therefore, this study proposes a hybrid robust decentralized energy management framework for the optimal day-ahead scheduling of interconnected MEMGs. Specifically, a decomposition strategy based on the alternating direction method of multipliers (ADMM) is utilized for privacy protection. The Wasserstein metric based distributionally robust optimization (DRO) and the robust optimization (RO) are incorporated to effectively deal with multiple uncertainties. Experimental results show that the coordination of multiple energy carriers (electricity, heat, hydrogen, and gas) can significantly lower the total cost and carbon emission cost (23.39% and 41.64%, respectively). Total cost can also be decreased by 3.15% with the implementation of integrate demand response (IDR). The used ADMM owns excellent performance, which can coverage to the result of centralized manner within a few iterations. Comparative analysis further demonstrated the predictability and superiority of the proposed hybrid robust optimization (HyRO).

A three-layer joint distributionally robust chance-constrained framework for optimal day-ahead scheduling of e-mobility ecosystem

A high number of electric vehicles (EVs) in the transportation sector necessitates an advanced scheduling framework for e-mobility ecosystem operation to overcome range anxiety and create a viable business model for charging stations (CSs). The framework must account for the stochastic nature of all stakeholders’ operations, including EV drivers, CSs, and retailers and their mutual interactions. In this paper, a three-layer joint distributionally robust chance-constrained (DRCC) model is proposed to plan day-ahead grid-to-vehicle (G2V) and vehicle-to-grid (V2G) operations for e-mobility ecosystems. The proposed three-layer joint DRCC framework formulates the interactions of the stochastic behaviour of the stakeholders in an uncertain environment with unknown probability distributions. The proposed stochastic model does not rely on a specific probability distribution for stochastic parameters. An iterative process is proposed to solve the problem using joint DRCC formulation. To achieve computational tractability, the second-order cone programming reformulation is implemented for double-sided and single-sided chance constraints (CCs). Furthermore, the impact of the temporal correlation of uncertain PV generation on CSs operation is considered in the formulation. A simulation study is carried out for an ecosystem of three retailers, nine CSs, and 600 EVs based on real data from San Francisco, USA. The simulation results show the necessity and applicability of such a scheduling framework for the e-mobility ecosystem in an uncertain environment, e.g., by reducing the number of unique EVs that failed to reach their destination from 272 to 61. In addition, the choice of confidence level significantly affects the cost and revenue of the stakeholders as well as the accuracy of the schedules in real-time operation, e.g., for a low-risk case study, the total net cost of EVs increased by 247.3% compared to a high-risk case study. Also, the total net revenue of CSs and retailers decreased by 26.6% and 10.6%, respectively.

Day-ahead optimal scheduling of a standalone solar-wind-gas based integrated energy system with and without considering thermal inertia and user comfort

Integrated energy system is an effective way to utilize renewable energy in a high proportion and reduce carbon emissions. However, renewable energy and load uncertainties bring great challenges to the optimal operation of the integrated energy system. The standalone integrated energy system shows the characteristics of excessive dependence on energy storage for the lack of large power grid support. To reduce the energy storage dependency and improve the scheduling optimization performance of the system, a standalone solar-wind-gas based integrated energy system (SWG-IES) is used to explore the relationship between uncertainties, thermal inertia and user comfort. Based on the characteristics of SWG-IES, day-ahead scheduling models with and without considering thermal inertia and user comfort are proposed. The optimization calculation and comparative analysis of four cases are carried out. Simulation results show thermal inertia and user comfort have significant impacts on day-ahead optimization strategy. After considering thermal inertia and user comfort, the utilization ratio of the battery and heat storage tank decreased by about 75 % and 30 % respectively, and abandoned heat occurred in certainty case. The function of thermal inertia and user comfort is equivalent to increasing the capacity of energy storage in the system, which has a positive impact on the large-scale and high-proportion use of renewable energy.

Optimal operation scheduling of household energy hub: A multi-objective optimization model considering integrated demand response

This paper proposes the multi-energy co-optimization of a household energy hub (EH) from the perspective of the supply side. In this paper, firstly, a household load optimization model is established with the minimization problem of the volatility of each load after participating in the integrated demand response. Secondly, to make this study more relevant to reality, an EH multi-objective optimization scheduling model is established in three dimensions: operation cost, carbon emission, and energy efficiency. Finally, a new α-constrained dominated differential evolution algorithm is proposed to obtain the Pareto optimal solution set for EH multi-objective optimization. Then, a compromise optimal day-ahead scheduling scheme is obtained using VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method. The algorithm used in this paper has better convergence and distributivity than the traditional non-dominant classification genetic algorithm II (NSGA-II) method with constrained internal penalty functions. The results show that users’ cost has dropped by an average of 30%, and energy efficiency has improved by 4.8%. The reduction in run time also allows for handling more significant amounts of load data.

MILP-based optimal day-ahead scheduling for a system-centric community energy management system supporting different types of homes and energy trading

Optimal day-ahead scheduling for a system-centric community energy management system (CEMS) is proposed to provide economic benefits and user comfort of energy management at the community level. Our proposed community includes different types of homes and allows prosumers to trade energy locally using mid-market rate (MMR) pricing. A mathematical model of the community is constructed and the optimization problem of this model is transformed into an MILP problem that can be solved in a short time. By solving this MILP problem, the optimization of the overall energy cost of the community and satisfaction of the thermal comfort at every home are achieved. For comparison, we also establish two different CEMSs for the same community: a prosumer-centric CEMS and no CEMS. The simulation results demonstrate that the community with the proposed CEMS has the lowest daily energy cost among three CEMSs. In particular, the community with the proposed CEMS only has 78% of the daily energy cost of the community with the prosumer-centric CEMS. Moreover, by using linear transformation, the computational time of the optimization problem of the proposed system-centric CEMS is only 118.2 s for a 500-home community, which is a short time for day-ahead scheduling of a community. We finally investigate the trade-off of the MMR pricing in the local energy trading of the community, which allows the profits of different types of homes to be flexibly adjusted.

Research on Multi-Energy Integrated Ship Energy Management System Based on Hierarchical Control Collaborative Optimization Strategy

The propulsion systems of hybrid electric ship output and load demand have substantial volatility and uncertainty, so a hierarchical collaborative control energy management scheme of the ship propulsion system is proposed in this paper. In a layer of control scheme, the traditional perturbation algorithm is improved. Increasing the oscillation detection mechanism and establishing the dynamic disturbance step length realizes the real-time stability of maximum power point tracking control. In the second-layer control scheme, the power sensitivity factor and voltage and current double closed-loop controller is introduced. By designing a two-layer coordinated control strategy based on the dynamic droop coefficient, the problem of voltage and frequency deviation caused by load switching is solved. In the third-layer control scheme, due to the need of the optimal scheduling function, the multi-objective particle swarm optimization algorithm was improved through three aspects: introducing the mutation factor, improving the speed formula, and re-initializing the strategy. Compared with other algorithms, this algorithm proves its validity in day-ahead optimal scheduling strategy. The superiority of the hierarchical collaborative optimization control schemes proposed was verified, in which power loss was reduced by 39.3%, the overall tracking time was prolonged by 15.4%, and the environmental cost of the diesel generator was reduced by 8.4%. The control strategy solves the problems of the steady-state oscillation stage and deviation from the tracking direction, which can effectively suppress voltage and frequency fluctuations.

A hybrid Robust-Stochastic optimization model for planned outage based Day-Ahead scheduling of a Plug-in electric vehicles parking lot

The growth of plug-in electric vehicles (PEVs) in recent decades and their impact on the operation of distribution networks have necessitated the requirement for PEV parking lots (PEPLs). With respect to the development of technology advances, PEPLs can benefit from charging the PEVs and selling the discharge power of PEVs (with V2G ability) to the grid. Besides, the load growth, the aging of the components and their thermal limitations enforce the distribution network operator to de-energize some of the zones to lighten the peak load. This de-energization with day-ahead notification is accounted for as a planned outage (PO). Such outages can have an adverse effect on the PEPL’s daily revenue. This paper introduces a hybrid robust-stochastic model for optimal day-ahead scheduling of the PEPL with renewable generation and considering the inherent uncertainties. The proposed model of the day-ahead scheduling is subject to the PO notified by the distribution network operator and concerns profit maximization in a way that mitigates the power shortage problem in the PO period. This PO-oriented scheduling helps the owner of the PEPL to successfully provide service for the PEVs at a minimum cost.

Day‐ahead optimal scheduling of building energy microgrids based on time‐varying virtual energy storage

The thermal inertia of a building envelope endows a building with a heat storage capability, introducing scheduling flexibility to a building energy microgrid (BEM). The flexibility is usually modelled as virtual energy storage (VES) and used to optimize the operation of BEMs to reduce electricity costs. However, the VES capacity is impacted by and varies with variations in indoor/outdoor temperature. If only the building envelope's effect on heat transfer is considered, without proper quantification of scheduling flexibility provided by the building envelope, the scheduling scheme (especially VES charging/discharging schemes) will deviate from the actual VES operating conditions, which may affect the thermal comfort of individuals in buildings or bring high electricity costs. In this paper, a time-varying building VES model (TVES) with three time-varying parameters (virtual electric capacity (VEC), state of charge (SOC), and charge and discharge power) is proposed to quantify the electricity storage capability of the VES at different time periods for participating in operation of the BEM. Based on the TVES, a day-ahead optimal scheduling strategy is developed for the BEM to simultaneously reduce the electricity cost and guarantee the user's thermal comfort, where the proposed time-varying parameters of TVES are taken as constraints in the optimization. Numerical studies verify the effectiveness of the proposed TVES and optimal scheduling strategy.

Optimal Peak Shaving Analysis of Power Demand Response Considering Unit Uncertainty

With the increasingly prominent environmental problems and the promotion of the “double carbon” goal, the participation of demand-side response resources in load peak shaving and valley filling is of great significance to the development of the power industry. In this paper, based on the background that demand-side response is being rapidly developed in China, we firstly consider the uncertainty of generating units and the operating constraints of power system and units, and construct the day-ahead output model of generating units by using stochastic production simulation and day-ahead optimal scheduling. Then, based on this model, we consider the impact of demand response on system peak shaving and build a demand response power system optimal scheduling model with unit uncertainty. Finally, the power supply and demand of the IEEE30 node system on two typical days of peak shaving considering demand response are analysed by MATLAB, and the minimum service cost of demand response and the amount of pull-out limit are derived, which clarify the important role of demand response in the process of power system peak shaving.

Optimal Dispatch of Multi-energy System Based on Carbon Trading Auxiliary Service

The energy economy, flexibility and low carbon of multi-energy system is one of the research hotspots of multi-energy system, a comprehensive energy system optimization scheduling method considering carbon trading is proposed. On the basis of considering the dynamic output of heat network equipment, an optimal dispatch model of the integrated energy system is constructed through energy coupling equipment such as cogeneration units, heat pumps, and gas turbines. Firstly, the thermal dynamics of heating pipes and heating buildings are modeled in detail. Then, taking the lowest operating cost of the integrated energy system and the lowest carbon transaction cost as the objective function, and taking the energy balance constraints and network constraints as the constraints, a day-ahead optimal scheduling scheme of the integrated energy system is proposed. Finally, the simulation results show that the multi-energy optimal scheduling method can effectively reduce the expenditure of the multi-energy system, increase the penetration ratio of wind and solar power generation, and reduce the overall carbon emissions of the system.

Day-Ahead Optimal Scheduling of Integrated Energy System Based on Type-II Fuzzy Interval Chance-Constrained Programming

Renewable energy sources (RES) generation has huge environmental and social benefits, as a clean energy source with great potential. However, the difference in the uncertainty characteristics of RES and electric–thermal loads poses a significant challenge to the optimal schedule of an integrated energy system (IES). Therefore, for the different characteristics of the multiple uncertainties of IES, this paper proposes a type-II fuzzy interval chance-constrained programming (T2FICCP)-based optimization model to solve the above problem. In this model, type-II fuzzy sets are used to describe the uncertainty of RES in an IES, and interval numbers are used to describe the load uncertainty, thus constructing a T2FICCP-based IES day-ahead economic scheduling model. The model was resolved with a hybrid algorithm based on interval linear programming and T2FICCP. The simulations are conducted for a total of 20 randomly selected days to obtain the advance operation plan of each unit and the operation cost of the system. The research results show that the T2FICCP optimization model has less dependence on RES output power and load forecasting error, so can effectively improve the economy of IES, while ensuring the safe and stable operation of the system.

A robust optimization framework for scheduling of active distribution networks considering DER units and demand response program

The widespread penetration of new resources of electricity and thermal generation and storage in active distribution networks (ADNs) has affected many issues of the distribution network, such as generation scheduling. In consideration of that, in this paper, a problem of simultaneous scheduling of CHP units, electrical and thermal storage, auxiliary boilers, distributed generation system, and responsive loads is studied considering the uncertainties of the network. In this situation, the operator of the ADN performs day-ahead optimal scheduling of the electrical and heat generation units, taking into account the possibility of participation of responsive loads and exchanging electricity with the upstream market to minimize operating costs. Moreover, the uncertainty of energy market prices is also modeled using information-gap decision theory (IGDT) and robustness and opportuneness functions. To solve the problem, a mixed-integer linear programming is proposed. Furthermore, the proposed model is implemented on the IEEE standard 18-bus system, and its efficiency is evaluated by using various scenarios and sensitivity analyses of the optimization problem. • Scheduling of CHP units, storages, boilers, DGs, and responsive loads is studied. • The uncertainty of electricity market energy prices is modeled. • The proposed problem is modeled using a robust planning scheme. • To solve the problem, a mixed-integer linear programming is proposed.

Stochastic multi-objectives optimal scheduling of energy hubs with responsive demands in smart microgrids

Energy hubs (EHs) are progressively being implemented in microgrids for local generation, transfer, and storage of various forms of energy. However, issues have emerged around developing an operating schedule for the different energy resources incorporated in the EHs to ensure the operation at minimum possible costs and emissions. This paper proposes a stochastic-based multi-objectives optimization model for optimal day-ahead scheduling of microgrids based on EHs. The proposed model simultaneously manages the non-dispatchable distributed generator units, such as wind turbines and photovoltaic systems, and energy storage systems, such as compressed air energy storage (CAES) and battery energy storage systems. Moreover, the thermal network is also established by introducing solar heat collectors, heat generated during the discharge of CAES, and thermal energy storage. Furthermore, the proposed model also considers the uncertainties of wind speed, solar radiation, and residential loads. A demand response program (DRP) is also employed to the residential and plug-in electric vehicles load to flatten the load curve and to perform more cost-effectively. A total reduction of 9.87 % and 21.41 % is achieved in the cost and emission amount, respectively, after applying the DRP. Furthermore, a decrease of 13.41 % and 45.04 % is also achieved for the costs of imported power and operation of the CAES, respectively. Different case studies are performed, and a comparative study with other existing research is conducted to demonstrate the effectiveness of the proposed approach. The simulation results show the efficacy of the proposed stochastic approach for optimal day-ahead scheduling.

Day-ahead optimal scheduling of power–gas–heating integrated energy system considering energy routing

Compared with independent scheduling, the coupled scheduling of integrated energy system can effectively reduce the operating cost and improve the consumption of renewable energy, which has become a major research hotspot. However, in an integrated system, uncertainties from one area could directly affect another, which could reduce the reliability and result in instability of the whole integrated energy system. This paper constructs a power–gas–heating integrated system based on energy routers and energy routing algorithm to study the day-ahead coordinated optimal scheduling strategy. At the same time, the power and natural gas flow are comprehensively considered, and the structure of the multi-port energy router with integrated energy and the energy routing algorithm are proposed to realize the power optimization and energy routing between the power, gas and heating networks. The numerical case studies illustrate that the energy router and proposed cooperative scheduling strategy can improve the energy supply reliability, scheduling flexibility, renewable energy consumption and economic benefits of the integrated energy system.

Resiliency assessment of the distribution system considering smart homes equipped with electrical energy storage, distributed generation and plug-in hybrid electric vehicles

This paper presents a novel method for resiliency assessment of the distribution system considering smart homes' arbitrage strategies in the day-ahead and real-time markets. The main contribution of this paper is that the impacts of smart homes' arbitrage strategy on the resilient operation of the distribution system are explored. The optimal commitment of smart homes in external shock conditions is another contribution of this paper. An arbitrage index is proposed to explore the impacts of this process on the system costs and resiliency of the system. A two-level optimization process is proposed for day-ahead and real-time markets. At the first stage of the first level, the optimal bidding strategies of smart homes are estimated for the day-ahead market. Then, the database is updated and the optimal bidding strategies of smart homes for real-time horizon are assessed in the second stage of the first level problem. At the first stage of the second level problem, the optimal day-ahead scheduling of the distribution system is performed considering the arbitrage and resiliency indices. At the second stage of the second level, the distribution system optimal scheduling is carried out for the real-time horizon. Finally, at the third stage of the second level, if an external shock is detected, the optimization process determines the optimal dispatch of system resources. The proposed method is assessed for the 33-bus and 123-bus IEEE test systems. The proposed framework reduced the expected values of aggregated costs of 33-bus and 123-bus systems by about 62.14 % and 32.06 % for the real-time horizon concerning the cases in which the smart homes performed arbitrage strategies. Furthermore, the average values of the locational marginal price of 33-bus and 123-bus systems were reduced by about 59.38 % and 63.98 % concerning the case that the proposed method was not implemented.

An Improved Load Forecasting Method Based on the Transfer Learning Structure under Cyber-Threat Condition.

Smart grid is regarded as an evolutionary regime of existing power grids. It integrates artificial intelligence and communication technologies to fundamentally improve the efficiency and reliability of power systems. One serious challenge for the smart grid is its vulnerability to cyber threats. In the event of a cyber attack, grid data may be missing; subsequently, load forecast and power planning that rely on these data cannot be processed by generation centers. To address this issue, this paper proposes a transfer learning-based framework for smart grid scheduling that is less reliant on local data while capable of delivering schedules with low operating cost. Specifically, the proposed framework contains (1) a power forecasting model based on transfer learning which can provide high quality load prediction with limited training data, (2) a novel adaptive time series prediction method with modeling time series from a covariate shift perspective that aims to train the forecasting model with a strong generalization capability, and (3) a day-ahead optimal economic power scheduling model considering a shared energy storage station.

Day‐ahead optimal dispatching of hybrid power system based on deep reinforcement learning

The problem of optimal dispatching of a power system containing a high proportion of renewable energy is of great significance for the realisation of new energy consumption and the economic and reliable operation of the power system. For the solution of non-linear, non-convex, multi-objective problems for the optimal operation design of a power system with wind and photovoltaic access, traditional methods have difficulties in terms of computational real-time and iterative convergence. To address this issue, a deep reinforcement learning-based optimal scheduling method for the hybrid power system is proposed, which enables continuous action control to obtain an optimal scheduling strategy through the interaction between the agent and the hybrid power system. Firstly, a mathematical description of the optimal scheduling problem containing wind power and photovoltaic power system is presented, and the state space, action space, and reward function of the agent are designed. Secondly, the basic framework of the deep reinforcement learning optimal scheduling model is constructed, and the basic principles of the twin delayed deep deterministic policy gradient algorithm are introduced. Finally, the effectiveness of the deep reinforcement learning model for day-ahead optimal scheduling of the hybrid power system is verified by means of an arithmetic analysis of the modified New England 39-bus system.

Multi-Objective Optimal Scheduling for Multi-Renewable Energy Power System Considering Flexibility Constraints

As renewable energy penetration increases, the lack of flexibility in a multi-renewable power system can seriously affect its own economics and reliability. To address this issue, three objectives are considered in this study: power fluctuations on tie-line, operating cost, and curtailment rate of renewable energy. Presented also is an optimal day-ahead scheduling model based on the MREPS for distributed generations with flexibility constraints. The multi-objective particle swarm optimization (MOPSO) algorithm can be applied to obtain a set of Pareto non-dominated solutions for the day-ahead scheduling strategy with the proposed model. By using fuzzy comprehensive evaluation, the optimal compromise solution is determined in the set. The presented method sacrifices a small amount of economy and power fluctuation, but it can reduce the deviation between forecast and realized power fluctuations on the tie-line, while improving the utilization of renewable energy.