The heat storage property of building envelopes is usually modeled into virtual energy storage (VES) and regarded as a flexibility resource to support the energy scheduling of building energy systems (BESs). However, the adjustable potential of VES is uncertain, incurred by several ambient random variables with/without specific probability distributions, posing challenges in determining the operational planning schemes of the BES. This article is intended to study a day-ahead optimal scheduling method for a PV-integrated BES (known as PV-BES) with the consideration of VES using interval optimization methods. First, an interval number is used to characterize the uncertainties of outdoor temperature, light irradiance, and the occupant’s behavior reflected by the uncontrollable household load. Second, an interval VES model is developed by modeling VES’s virtual charge–discharge power (VCDP) with an interval number. Finally, a day-ahead optimal interval scheduling model for the PV-BES considering VES is formulated, aiming to minimize the electricity energy purchase cost of the PV-BES from the external grid. Numerical simulation is conduced, and the results validated the effectiveness of the proposed method.
In the context of carbon neutrality, the electric vehicles (EVs) are increasingly used in city logistics service. To utilize EVs in the logistics delivery service in a cost-effective manner, this paper proposes a coordinated operational planning method for EVs considering the real road networks and battery swapping for EVs. Specifically, utilizing the EVs parked in the depot to discharge to power the depot or feed to the grid for an arbitrage, this paper is to minimize the sum of transportation cost and energy cost by determining the optimal number of EVs assigned to delivery goods and their driving route, as well as the charge–discharge operation decisions of all EVs including those without a delivery assignment and those returning to the depot after completing delivery. In simulation study, the effectiveness of the proposed methods is finally validated.
This paper presents a bilevel planning framework to coordinate truck mobile chargers (TMCs) and fixed chargers (FCs) on highways to promote charging flexibility and provide more choices for electric vehicle (EV) users. A collaborative location optimization (CLO) approach is developed at the upper level to optimize the location of charging stations. Furthermore, a collaborative capacity optimization (CCO) approach is formulated at the lower level to optimize the capacity of TMC and FC at candidate stations. In the proposed framework, origin-destination (OD) analysis, Floyd algorithm and Monte Carlo simulation (MCS) are employed to generate the spatial-temporal distribution of charging demand based on historical data. An improved income approach (IIA) is then developed to well capture the heterogeneity of EV users' charging behavior. The waiting cost of EV users is estimated by their value of time (VOT), which helps them to make a better choice between TMC and FC. To solve the optimal model easily, the big-M method is applied to linearize and convert the nonlinear problem into a mixed-integer linear programming (MILP) model. Meanwhile, the analytical target cascading (ATC) technique is employed to realize the data exchange process between the upper and lower layers. Finally, numerical study demonstrates the effectiveness of the proposed framework and method.
To relieve the impact of the power grid outages on the residential sector, residential building energy management under grid outage events has been becoming the research hotspot in both the academic and industry. Considering the coordination of electric vehicles (EVs) and household load flexibility, this paper proposes a two-stage energy management approach for residential community under the planned outages. In the optimal scheduling stage, after receiving the information of the planned outage from the grid, the EVs' charging–discharging power and community load curve reshaping schemes are optimal determined by residential community energy management system (CEMS), aiming at to minimize the total amount of unserved load of the whole community over the planned outage horizon. In the power allocation stage, with the formulated power allocation model, the determined residential community load curve from the above stage is then allocated to each house. The numerical test is finally conducted and the results validated the effectiveness and feasibility of the proposed approach.
Car sharing has become an increasingly popular mode of travel. This paper provides a comprehensive literature survey on relocation optimization for shared electric vehicles. The literature is reviewed and categorized based on two types of relocation: static and dynamic relocation. Static relocation is analyzed in terms of operator-based relocation and user-based relocation, while dynamic relocation is analyzed in terms of four methodologies: optimization models, simulations, multi-stage methods, and deep reinforcement learning. The paper finally provides some interesting future research topics, such as considering the nonlinear charging process of electric vehicles in the process of constructing relocation optimization models and designing algorithms for shared electric vehicles.
The integrated energy system (IES) is recognized as an effective measure to promote energy efficiency and environmental protection. However, the uncertain efficiency of energy devices under variant working conditions threatens the operation of the IES. In this paper, a low-carbon economic dispatch method for the IES considering the uncertainty of energy efficiency is developed. Specifically, a dynamic energy hub (DEH) is established by integrating an efficiency correction technique into the traditional energy hub (EH). The deep neural network (DNN) method with excellent accuracy in nonlinear mapping is utilized to correct energy efficiency affected by the load level, temperature and pressure. Based on the DEH, a low-carbon economic dispatch model is formulated to minimize operational costs. Case studies verify the effectiveness of the proposed method, which can enhance the accuracy of dispatch schemes and simultaneously, promote the low-carbon economic dispatch of the IES.
The integrated energy system (IES) is recognized as a promising energy utilization approach enabled to both improve the energy efficiency and reduce pollutant emissions. Although the economic-environmental dispatch (EED) problem of the IES has been widely studied, the fact that the IES is operated under off-design conditions, having a significant influence on the efficiency of energy devices, is neglected usually, resulting in the scheduled operations, for the IES could not be accurate enough in many actual situations. This study investigates the EED problem of the IES under off-design conditions. Technically, by integrating an efficiency correction process into the traditional energy hub (EH) model, a dynamic energy hub (DEH) model is first formulated for adapting itself to variable energy conversion efficiencies. Then, a deep neural network (DNN)-based efficiency correction method is proposed to predict and correct the time-varying efficiency of energy devices based on three main off-design conditions including the load rate, air temperature, and pressure. A multi-objective EED model is finally formulated for the IES, with the framework of the DEH model, aiming at establishing a trade-off between operational cost and emitted pollutants. Case studies show that the proposed approach helps in enhancing the accuracy of IES dispatch while having a good performance in both the operational cost and pollutant emission reduction.
This paper has proposed an electric vehicle (EV) route selection and charging navigation optimization model, aiming to reduce EV users' travel costs and improve the load level of the distribution system concerned. Moreover, with the aid of crowd sensing, a road velocity matrix acquisition and restoration algorithm is proposed. In addition, the waiting time at charging stations is addressed based on the queue theory. The formulated objective of the presented model is to minimize the EV users' travel time, charging cost or the overall cost based on the time of use price mechanism, subject to a variety of technical constraints such as path selections, travel time, battery capacities, and charging or discharging constraints, etc. Case studies are carried out within a real-scale zone in a city where there are four charging stations and the IEEE 33-bus distribution system. The effects of real-time traffic information acquisition and different decision targets on EV users' travel route and effects of charging or discharging of EVs on the load level of the distribution system are also analyzed. The simulation results have demonstrated the feasibility and effectiveness of the proposed approach.
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