Uncertainty Of Electric Vehicles Research Articles

Price-based demand response with renewable energy sources and peer-to-peer trading for residential microgrid with electric vehicle uncertainty

This article deals with the impact of demand response (DR) with peer-to-peer (P2P) energy trading for a residential smart home consisting of smart appliances, an electric vehicle, a battery energy storage system (BESS), and renewable energy-based distributed generation (DG) such as solar PV and wind. The proposed work deals with two stages, such as DR implementation and P2P energy trading. The first stage of implementation deals with the optimal scheduling of smart appliances in a residential home using a Binary Dragonfly Algorithm (BDA). Each appliance in the smart home is scheduled based on the slab tariff, Time of Day (ToD), and Real-Time Pricing (RTP) with DR to identify the most economical tariff structure for the residential home. Five different cases are examined to identify the best smart home for effectively utilizing DGs by reducing grid dependency and electricity costs.The RTP tariff is proved to be more beneficial than the slab tariff and ToD tariff because the electricity cost of the smart home is very minimal for all the cases with RTP, especially in case 5 cost is reduced by ₹12.0613 than slab tariff and ₹8.414 than ToD. Hence, the RTP tariff is chosen as the optimal tariff structure for the next stage of the proposed work.The second stage of implementation deals with the P2P trading between the smart homes with a proposed enhanced bidding strategy to reduce the grid dependency and electricity cost of the individual smart homes. The proposed enhanced bidding strategy involves a double auction mechanism to determine the trading decision and trading cost for the benefit of both consumers and prosumers. The electricity cost reduction achieved by consumer 1 and consumer 2 from the enhanced smart bidding strategy are ₹92.7615 and ₹15.1525, respectively. The results also proved that prosumer 1 and prosumer 2 obtained a profit of ₹78.2170 and ₹28.6970, respectively. The EV uncertainty analysis is also examined in smart homes to validate the effectiveness of the proposed bidding strategy. The simulation results proved that the proposed DR-based P2P trading with an enhanced bidding strategy reduces grid dependency and electricity costs, including EV uncertainty conditions.

Quantifying and enabling the resiliency of a microgrid considering electric vehicles using a Bayesian network risk assessment

With the increase in the number of natural disasters, caused by climate change, the role of emission and resilience has become more outstanding. In this paper, a novel model for quantifying and enabling the resiliency of a microgrid, using a Bayesian network risk assessment approach is presented. The three objective functions of cost, pollution, and resilience are optimized simultaneously using an improved lexicographic augmented ɛ-constraint method. It should be mentioned that these items are included in the proposed model due to the irreplaceable role of electric vehicles (EVs) and renewable energy sources (RESs) in distribution systems. Moreover, with the Bayesian network, the interaction between random variables is considered and a graphic view of casual relationships between stochastic and probabilistic variables is investigated in this study. The Monte Carlo scenario-based technique has handled the uncertainties of EVs and RESs. The proposed mixed integer linear programming (MILP) is implemented in the GAMS software environment under the CPLEX solver and finally, the results are discussed.

Two-stage stochastic planning for distribution networks considering uncertainties of electric vehicles and distributed generators

Abstract A significant quantity of electric vehicles and distributed generator (DG) sources are connected to the distribution network (DN), resulting in the formation of a new power-transportation network (PTN). This integration introduces source-load uncertainty to the system. This research presents a stochastic planning model for PTN that focuses on making decisions on charging station locations. Initially, a model for the distribution network is created, which incorporates energy storage devices, demand response devices, and DG devices. The transportation network utilizes a P-median siting determination model. Ultimately, a two-stage stochastic planning approach is employed to address system uncertainty. The primary goal is to minimize both the investment cost and operation cost of the system while also considering several common situations. The efficacy of the proposed approach has been proven using a combined system consisting of a 33-node DN and a 25-node TN.

Distributed and Multi-Agent Reinforcement Learning Framework for Optimal Electric Vehicle Charging Scheduling

The increasing number of electric vehicles (EVs) necessitates the installation of more charging stations. The challenge of managing these grid-connected charging stations leads to a multi-objective optimal control problem where station profitability, user preferences, grid requirements and stability should be optimized. However, it is challenging to determine the optimal charging/discharging EV schedule, since the controller should exploit fluctuations in the electricity prices, available renewable resources and available stored energy of other vehicles and cope with the uncertainty of EV arrival/departure scheduling. In addition, the growing number of connected vehicles results in a complex state and action vectors, making it difficult for centralized and single-agent controllers to handle the problem. In this paper, we propose a novel Multi-Agent and distributed Reinforcement Learning (MARL) framework that tackles the challenges mentioned above, producing controllers that achieve high performance levels under diverse conditions. In the proposed distributed framework, each charging spot makes its own charging/discharging decisions toward a cumulative cost reduction without sharing any type of private information, such as the arrival/departure time of a vehicle and its state of charge, addressing the problem of cost minimization and user satisfaction. The framework significantly improves the scalability and sample efficiency of the underlying Deep Deterministic Policy Gradient (DDPG) algorithm. Extensive numerical studies and simulations demonstrate the efficacy of the proposed approach compared with Rule-Based Controllers (RBCs) and well-established, state-of-the-art centralized RL (Reinforcement Learning) algorithms, offering performance improvements of up to 25% and 20% in reducing the energy cost and increasing user satisfaction, respectively.

Robust Operation of Flexible Distribution Network With Large-Scale EV Charging Loads

The increasing integration of electric vehicles (EVs) significantly challenges the efficient operation of distribution networks. With the rapid development of power electronic devices represented by soft open point (SOP), flexible distribution networks (FDNs) provide a promising opportunity for integrating large-scale EVs. In this paper, a robust operation method is proposed to improve the economy of EVs and ensure the flexible operation of FDN. First, an EVs-aggregator (EVs-AGG) model is formulated to incorporate diverse charging behaviors. The EVs-AGG is efficiently integrated into FDN through the DC link of multi-terminal SOP. Then, optimal operation strategies are obtained by coordinating EVs charging/discharging power with the regulation of multi-terminal SOP. Furthermore, considering the uncertainties of EVs and photovoltaics, a robust operation framework is established to deal with uncertainties. Finally, case studies based on a practical distribution network indicate that through the interactive power regulation between multi-terminal SOP and EVs-AGG, the proposed method alleviates the negative impacts caused by large-scale EVs integration and improves the operational flexibility of FDN.

Distributed energy management of electric vehicle charging stations based on hierarchical pricing mechanism and aggregate feasible regions

With the rapid development of electric vehicle charging stations, effective management of them has become challenging due to the high uncertainty of electric vehicles, the pricing mechanisms of charging stations, and their coupling with distribution networks. To address these challenges, this paper proposes a two-stage framework for energy management at charging stations. In the first stage, a resource allocation model considering the profits of distribution systems, charging stations, and electric vehicle users is established based on the aggregate feasible power regions of charging stations. The aggregate feasible region is obtained based on the combination of Minkowski summation and the data-driven method, which can preserve the privacy of electric vehicle data and reduce the computational burden. In the second stage, a novel hierarchical pricing mechanism is developed, which encompasses both the clearing price between charging stations and distribution networks and the retail electricity price between charging stations and electric vehicle users. Notably, charging stations participate in the power clearing of distributed networks based on the aggregate feasible power region, while a two-stage robust pricing strategy is established between electric vehicle users and charging stations. The model is finally optimized through a distributed coordination mechanism with a clear physical interpretation. The simulation results show that the proposed aggregation method enables charging stations to achieve a total economic profit at least 1.76 % higher than three competitive methods. The hierarchical pricing mechanism allows charging stations to achieve total economic profits 18.60 % and 2.94 % higher than those in the centralized dispatch and price-taker modes, respectively, while simultaneously reducing operating costs for the distributed network by 25.96 % and 27.99 %.

A Fast Polytope-Based Approach for Aggregating Large-Scale Electric Vehicles in the Joint Market Under Uncertainty

The aggregation of electric vehicles (EVs) has been advocated as an effective means to manage and control large-scale plug-in EVs. However, the efficient aggregation is challenging due to the heterogeneity and uncertainty of individual EVs. To cope with this challenge, this paper puts forth a fast polytope-based aggregation approach, which could deal with the time-coupled, uncertain, and heterogeneous individual EV models. First, the feasible region of each EVs power profiles is expressed as a polytope under H-representation considering various operational constraints. To improve the aggregation efficiency, H-representation reconstruction and polytopic projection processes are designed to address the heterogeneity of EVs. The uncertainties of EVs are modeled by the distributionally robust joint chance-constrained programming. The original individual and aggregate feasible region of EVs are subsequently inner approximated by the homothets of a basic prototype. Last, a day-ahead optimal bidding model for charging service provider in a joint market, including energy, peak-shaving and reserve markets, is developed. Numerical simulation results based on real-world dataset demonstrate that the proposed aggregation method can improve both the computation efficiency and accuracy of approximate feasible region.

An Analytical Polytope Approximation Aggregation of Electric Vehicles Considering Uncertainty for the Day-Ahead Distribution Network Dispatching

The integration of large-scale heterogeneous electric vehicles (EVs) into the distribution network increases the system management complexity significantly. Due to the flexible charging/discharging operation and shiftable energy consumption during the parking time, EVs possess a great dispatching potential for the distribution network. This paper develops an analytical polytope approximation (APA) aggregation model to efficiently depict the dispatchable regions of large-scale EVs with uncertainties and proposes a bilevel cooperative optimization approach for EV aggregators to participate in the distribution network day-ahead optimal scheduling. Uncertainties of EVs are modeled by chance constraints with different confidence levels to accommodate various regulatory requirements. The proposed approach can obtain large-scale EVs dispatchable capabilities with minimum flexibility loss and realize the regulation command disaggregation without violations. Numerical case studies are conducted on IEEE 33-bus and 141-bus distribution networks based on real data sets, which show that the proposed approach outperforms other state-of-art methods in terms of low operation flexibility conservatism, high system economics, and computational efficiency.

A Two-Stage Scheduling Strategy for Electric Vehicles Based on Model Predictive Control

In recent years, with the rapid growth in the number of electric vehicles (EVs), the large-scale grid connection of EVs has had a profound impact on the power grid. As a flexible energy storage resource, EVs can participate in auxiliary services of the power grid via vehicle-to-grid (V2G) technology. Due to the uncertainty of EVs accessing the grid, it is difficult to accurately control their charging and charging behaviors at both the day-ahead and real-time stages. Aiming at this problem, this paper proposes a two-stage scheduling strategy framework for EVs. In the presented framework, according to historical driving data, a day-ahead scheduling model based on distributionally robust optimization (DRO) is first established to determine the total power plan. In the real-time scheduling stage, a real-time scheduling model based on model predictive control (MPC) is established to track the day-ahead power plan. It can reduce the impact of EVs’ uncertainties. This strategy can ensure the charging demand of users is under the control of the charging and discharging behaviors of EVs, which can improve the accuracy of controlling EVs. The case study shows that the scheduling strategy can achieve accurate and fast control of charging and discharging. At the same time, it can effectively contribute to the security and stability of grid operations.

Optimal dispatch of a mobile storage unit to support electric vehicles charging stations

AbstractAs transportation electrification increases globally, new technologies emerged in the past few years to meet the growth of the electricity demand. Mobile Energy Storage Systems (MESS) offer versatile solutions, aiding distribution systems with reactive power, renewables integration, and peak shaving. An MESS can be utilized to serve electric vehicles (EVs) in different parking lots (PLs), in addition to supplying power to the grid during overloads. The task of multiple stationary units can be achieved using MESS at a relatively lower cost. This paper proposes an MESS owned by multiple PLs sharing the same geographical area and sharing its capital and operational cost. The main objective of the proposed approach is to dispatch the MESS in conjunction with optimal EVs’ charging coordination to minimize operational costs and address the extra demand of PLs. A mixed‐integer nonlinear programming (MINLP) problem is formulated and solved. Considering electricity price variations and EVs uncertainties, three different case studies are performed to highlight the efficiency and success of the proposed approach. The simulation results in a huge reduction in the total operation cost and the savings reach up to 27.51% in comparison with the base case.

Optimal operation of microgrid with consideration of V2G's uncertainty

AbstractThe aggregation of the remaining battery capacity of electric vehicles (EVs) can be used as distributed energy storage to participate in the microgrid optimisation through vehicle‐to‐grid (V2G) technology. However, the reliability of this service to be delivered is affected by the uncertainty of EVs’ behaviour. The un‐reliable service will bring risks in the operation of microgrid when EVs’ V2G electricity is regarded as an important flexibility resource. This paper, first proposes an analytical method to quantify the reliability of EV aggregation's (EVA's) V2G electricity based on the analysis of historical charging data and compound Poisson process. Based on this model, electric vehicles aggregators can evaluate the V2G bid quantity for any time slot on day t+1 according to the reliability requirements. Secondly, an optimal operation model of microgrid with consideration of V2G's reliability is considered. Finally, a test system with photovoltaics (PVs) and EVA on top of the original IEEE 33‐node system is used to verify the effectiveness of the proposed model. The results show that with the proposed reliability evaluation method of V2G electricity, the optimal operation of the microgrid can be reached with appropriate evaluation of the capability of the EVA.

Optimal bidding and coordinating strategy for maximal marginal revenue due to V2G operation: Distribution system operator as a key player in China's uncertain electricity markets

This paper proposes a novel management scheme, namely, the distribution system operator (DSO) as a key player participating in China's electricity markets owing to its high value-added capability, high customer loyalty and robust risk resistance. In light of this, this paper discusses the maximal marginal revenue for DSO in uncertain electricity markets with vehicle-to-grid operation incorporated. Initially, considering different benefit expectations of electric vehicles (EVs) owners, DSO could provide preferential contracts, including the discount price contract (DPC) and revenue-sharing contract (RSC), in exchange for the management rights of EVs. Then, the stochastic optimization model considering the spatiotemporal uncertainties of EVs and electricity markets is built up, and the optimal bidding and coordinating strategy are proposed to maximize the marginal revenue of DSO. Comprehensive case studies are conducted in a 31-node distribution system of a university campus under the electricity markets in Guangdong, China. Results reveal that different distribution of EVs parking lots has a significant impact on the profit of DSO, and the revenue for DSO is 13,477 yuan more than that for EVA by scheduling 1400 EVs' charging/discharging behavior properly. Additionally, the DSO with RSCs is exposed to 25% less market volatility risk than that with DPCs.

Optimal integration of CCHP with electric Vehicle parking lots in energy hub

Nowadays, due to environmental problems and the lack of fossil fuels, the use of Electric Vehicles (EVs) is extended. These systems need optimal planning and management, like all energy tools. With another look at electrical generation systems, the combined energy production units have also increased dramatically, with only one kind of fuel, it is possible to produce any kind of energy such as electrical, thermal, and cooling energies at the same time. This production type is called the energy hub. The main goal of creating an energy hub is to meet several types of demand at the same time and to increase the flexibility of the system. The Combined Cooling, Heat, and Power (CCHP) unit, which has a higher efficiency ratio to traditional systems, could be referred to as the main component of the energy hub. To reduce the losses of these systems, their planning could be implemented in coordination with the EV parking lot. The energy hub system could provide the mentioned conditions in the presence of renewable energy sources and energy storage units in a coordinated and optimal way. In this paper, the presence of an EV parking lot in an energy hub is investigated that includes a CCHP, a photovoltaic unit in the presence of demand response programs, and its effects on optimizing power consumption to reduce cost. The demand response programs are used to reduce the operating costs of the energy hub. Moreover, the uncertainties of EV consist of entry and exit times and charge level, solar irradiation, and loads value are applied in the model. Finally, the cost of risk by the conditional value–at–risk method is investigated. The simulations are done by determining 3 various case studies for better investigation of the proposed model. The results show that the proposed solution method of the demand response program could decrease the total cost of the energy hub by about 1.90 % in both summer and winter, under risk-averse conditions.

Electric vehicle charging and discharging scheduling strategy based on dynamic electricity price

The rapid growth in the number of electric vehicles (EVs) has significantly increased the demand for electricity for residents. In addition, because the charging time of EVs highly coincides with the peak period of user electricity consumption, the disorderly charging of EVs will lead to the overload of the power grid transformer. Traditional control methods lack certain robustness and do not fully consider the uncertainty of EVs. As a result, the V2G participation rate of electric vehicles cannot be determined, and the control reliability is low. To solve the above problems, this paper designs a reinforcement learning framework of Long Short-Term Memory network and Improved Linear programming algorithm (LSTM-ILP) to control the V2G of EVs.This paper comprehensively considers the overall electric vehicle charging demand, discharge potential, large grid electricity price, aggregator, and users’ interests demands. Firstly, aiming to minimize the charging and discharging fee of EVs and the load peak-to-valley difference of the power grid, a dynamic electricity price based on Long Short-Term Memory neural network (LSTM) is established. Then, the improved linear programming algorithm (ILP) is used to solve the charging and discharging optimization problem of EV, and the results are fed back to the input of the next iterative update of the LSTM, and finally, the optimal electricity price and EV charging and discharging schedule are achieved. The simulation results show that the LSTM-ILP framework can not only reduce the charging fee of electric vehicles, but also achieve the Peak and valley trimming of the grid load. Charging costs for EV users were reduced by 42.1% compared with unordered charging, and by 22% percent compared with orderly charging.

Distributionally robust optimal dispatching method of integrated electricity and heating system based on improved Wasserstein metric

This paper proposed a distributionally robust optimal dispatching (DROD) method of integrated electricity and heating system based on an improved Wasserstein metric, for dispatching problems to manage the risk from uncertain wind power forecasted errors and operating characteristics of electric vehicles (EVs). The ambiguity set employed in the distributionally robust formulation for wind power forecasted error is the Wasserstein ball centered at the empirical distribution and an adjustable uncertainty set of EV operating characteristics is proposed in this model to quantify the uncertainty of EVs more flexibly and intuitively. The proposed framework minimizes the generating cost, EV operating cost, and the expected generation units adjustable cost under the worst-case distribution in the uncertainty set. To improve the computational efficiency of the proposed DROD model, an improved Wasserstein metric based on extreme scenarios DROD (WMES-DROD) model is proposed to construct the ambiguity set of wind power forecasted error. The comparison between the Wasserstein metric-based DROD and WMES-DROD indicates that the proposed WMES-DROD model can obtain optimal results with less time. The out-of-sample analysis with the IEEE 118-bus test system confirms the excellent robustness and computational superiority of the proposed model.

Bridging electricity market and carbon emission market through electric vehicles: Optimal bidding strategy for distribution system operators to explore economic feasibility in China's low-carbon transitions

China, as the world's largest emitter of carbon emissions, has implemented carbon markets to reduce carbon emissions, but the economic feasibility of integrating electric vehicles (EVs) into these markets remains uncertain. To address this challenge, we propose a novel methodology for grid integration of EVs based on the Shenzhen carbon inclusive management measures. Our proposed methodology can optimize EV charging schedules based on carbon emission prices and improve the economic feasibility of low-carbon transitions through EVs in China. We incorporate the Black-Scholes model to measure carbon emission option pricing and develop an optimal bidding strategy that considers spatiotemporal uncertainties of EVs and electricity markets. We use numerical simulations with real-world data from Shenzhen carbon emission market and Guangdong electricity market to demonstrate the effectiveness of our proposed methodology. Results reveal that scheduling 100 EVs' charging/discharging behavior properly can generate a revenue of 2379.4 yuan for the distribution system operator, while the revenue for carbon emission reduction (CER) is only 9.35 yuan. This paper demonstrates that the potential revenue of CER may be substantially based on the future scenario for the carbon emission market. These findings provide valuable insights for policymakers and stakeholders seeking to reduce carbon emissions in China's electricity sector.

Optimal scheduling strategy for orderly charging and discharging of electric vehicles based on spatio-temporal characteristics

With the development of vehicle-to-grid (V2G) technology, the disorderly access of a large number of electric vehicles (EVs) will impact the operation of the power system. Considering the uncertainty of EVs charging/discharging load on the spatio-temporal scale, this paper proposes an optimal scheduling strategy for EVs with spatio-temporal characteristics. The strategy integrates EVs into the microgrid, constructs the spatio-temporal distribution models of charging load and describes the uncertainty of electric vehicle owners’ (EVOs) charging behaviours by fuzzy theory. Considering the bounded rationality of EVOs, this paper takes the charging/discharging price of EVs as the decision variable, and the dynamic time-of-use price mechanism is used to guide EVOs to make charging/discharging decisions. Then, with the objective function of minimising the comprehensive operating cost (COC) and the peak-valley load difference (PVLD), an orderly charging/discharging scheduling model for EVs is established and the optimal scheduling strategy is given. Furthermore, the three scheduling scenarios are set up in the case study, and the maximum number of EVs to participate in scheduling is determined to be 4000. The numerical results show that the proposed scheduling strategy reduces the COC and PVLD by 17.96% and 5.21%, respectively, compared with the disorderly scheduling, and reduces the COC and PVLD by 13.35% and 5.21%, respectively, compared with the orderly scheduling without V2G, which verifies the effectiveness and superiority of the proposed scheduling strategy.

A novel information gap decision theory‐based demand response scheduling for a smart residential community considering deep uncertainties

AbstractDemand response programs (DRPs) have paved a meaningful role in the power supply–demand balance in a smart grid. Also, a residential community with the presence of renewable energy sources (RESs) and electric vehicles (EVs) provides a new way to tackle growing concerns about energy efficiency and environmental pollution. The inherent uncertainty of RESs generation and EVs behaviour leads to difficulty in the economic scheduling of the demand side. Different types of uncertainty modelling have been investigated, such as Monte Carlo (MC) simulation, fuzzy method, and robust optimization. They are faced with many scenarios and computational complexity. This paper uses the information gap decision theory (IGDT) method to study variations of uncertainty radius on residential community electricity costs. Therefore, to achieve an optimal strategy for scheduling the appliances considering the deep uncertainties of RESs and EVs, a novel IGDT‐based demand response scheduling for a residential community is proposed. Impacts of different levels of uncertainties are studied. The simulation results depict the privileges of the proposed method when confronting deep uncertainties. By increasing the radius of the uncertainty of RES and the initial charge of EVs, energy consumption costs grew 20% and 2%, respectively, which indicates the system operator can manage the costs effectively.

Optimal Operation of Multi-microgrid System Considering Uncertainty of Electric Vehicles

Optimal Operation of Multi-microgrid System Considering Uncertainty of Electric Vehicles

Risk-embedded scheduling of a CCHP integrated with electric vehicle parking lot in a residential energy hub considering flexible thermal and electrical loads

Climate change has detrimental impacts on the environment and sustainability, leading to employing alternative energy systems such as combined cooling, heat and power (CCHP). The concept of integrated energy systems (IES) allows the coordination of several components, such as electric vehicles (EV), to serve various demands simultaneously. This paper focuses on coordinating the CCHP and electric vehicle parking lot (EVPL) integrated with photovoltaic (PV) technology as renewable energy (RE). The residential energy hub (REH) is modeled to integrate these components to meet the demands and minimize REH’s operating costs and carbon emissions. EVPL functions as dynamic electrical storage besides serving EVs. Stochastic programming is used to model RE, EV, loads, and electricity price uncertainties. Demand response (DR) is applied for shiftable electrical loads. The thermodynamic model of heating and cooling loads is developed with flexibility as integrated demand response (IDR) based on the building’s desired temperature. The emission cost model with penalty factors enforces REH to use less-pollutant energy sources. Subsequently, a risk-aversion strategy, namely downside risk constraint (DRC), is implemented to diminish the associated risk as the consequence of uncertainties for the decision-maker. Different constraint level is applied to provide various conservative decision-making strategies for the operator. Summer and winter scenarios with and without DR and flexible thermal loads were used to evaluate the model’s accuracy. The scheduling problem is solved in IEEE 33-bus test system. The results reveal that the DR could reduce the operation cost by 5% in summer and 8% in winter. Moreover, zero risk for summer and winter is gained at the cost of 10.4% and 3.1% increment in operating costs.