Probabilistic Charging Power Forecast of EVCS: Reinforcement Learning Assisted Deep Learning Approach

The electric vehicle (EV) and electric vehicle charging station (EVCS) have been widely deployed with the development of large-scale transportation electrifications. However, since charging behaviors of EVs show large uncertainties, the forecasting of EVCS charging power is non-trivial. This paper tackles this issue by proposing a reinforcement learning assisted deep learning framework for the probabilistic EVCS charging power forecasting to capture its uncertainties. Since the EVCS charging power data are not standard time-series data like electricity load, they are first converted to the time-series format. On this basis, one of the most popular deep learning models, the long short-term memory (LSTM) is used and trained to obtain the point forecast of EVCS charging power. To further capture the forecast uncertainty, a Markov decision process (MDP) is employed to model the change of LSTM cell states, which is solved by our proposed adaptive exploration proximal policy optimization (AePPO) algorithm based on reinforcement learning. Finally, experiments are carried out on the real EVCSs charging data from Caltech, and Jet Propulsion Laboratory, USA, respectively. The results and comparative analysis verify the effectiveness and outperformance of our proposed framework.Comment: Accepted by IEEE Transactions on Intelligent Vehicle

Research on the Optimized Operation of Hybrid Wind and Battery Energy Storage System Based on Peak-Valley Electricity Price

The combined operation of hybrid wind power and a battery energy storage system can be used to convert cheap valley energy to expensive peak energy, thus improving the economic benefits of wind farms. Considering the peak–valley electricity price, an optimization model of the economic benefits of a combined wind–storage system was developed. A charging/discharging strategy of the battery storage system was proposed to maximize the economic benefits of the combined wind–storage system based on the forecast wind power. The maximal economic benefits were obtained based on scenario analysis, taking into account the wind-power forecast error, and costs associated with the loss of battery life, battery operation, and maintenance. Case simulation results highlight the effectiveness of the proposed model. The results show that the hybrid wind–storage system is not only able to convert cheap electricity in the valley period into expensive electricity in the peak period, thus resulting in higher economic benefits, but can also balance the deviation between actual output and plans for the wind power generator to decrease the loss penalty. The analyzed examples show that, following an increase in the deviation of the forecast wind power, the profit of the combined wind–storage system can increase by up to 45% using the charging/discharging strategy, compared with a wind farm that does not utilize energy storage. In addition, the profit of the combined wind–storage system can increase by up to 16% compared with separate systems, following an increase in the deviation penalty deviation coefficient

Multi-Flexibility Resources Planning for Power System Considering Carbon Trading

Clean and low-carbon energy represented by wind power and photovoltaic power will develop rapidly and will form a new power system with a high proportion of renewable energy. In the context of a low-carbon economy, how to make reasonable planning for power system flexibility resources is crucial for the development of new power systems. In this paper, we establish a multi-flexibility resource planning model for a power system based on a low carbon economy by considering the planning of multi-flexibility resources of “source–load–storage”. First, a ladder-type carbon trading cost accounting model is proposed, and a set of power system flexibility evaluation indexes are proposed. Then, with the objective of minimizing the sum of low carbon operation cost, investment cost, and operation cost of the system, the planning model of multi-flexibility resources is established by considering constraints such as system power balance constraint, investment constraint, and wind power consumption constraint. Finally, the model proposed in this paper is validated by the IEEE-RTS96 system; the results show that: (1) collaborative planning of source–load–storage multi-flexible resources can obtain the best overall system economics, although the investment cost increases by USD 12.6M, the total system cost is reduced by 11.22% due to the reduction in coal generation consumption cost, carbon trading cost, and wind curtailment penalty cost; (2) as the penetration of wind power grows, the demand for energy storage in the power system is gradually increasing; when the installed capacity of wind power grew from 800 MW to 1600 MW, the demand for new thermal power decreased by 53.5% and the demand for new energy storage increased by 200%; (3) the total cost of the planning model considering ladder-type carbon trading decreases by 1.35% compared to the model without carbon trading, and increases by 2.5% compared to the model considering traditional carbon trading, but its carbon emissions decrease by 5.5%

Optimization of Expressway Microgrid Construction Mode and Capacity Configuration Considering Carbon Trading

An expressway microgrid can make full use of renewable resources near the road area and enable joint carbon reduction in both transportation and energy sectors. It is important to research the optimal construction mode and capacity configuration method of expressway microgrid considering the carbon trading and carbon offset mechanism. This paper establishes a design model for an expressway microgrid considering the operating features of each component in the microgrid under two patterns of grid-connected/islanded and two types of AC/DC. The goal of the proposed model is to minimize the annualized comprehensive cost, which includes the annualized investment cost, operational cost, and carbon trading cost. The model designates the optimal construction mode of an expressway microgrid, i.e., grid-connected or islanded, AC or DC. As a mixed integer nonlinear programming (MINLP) problem, the proposed model can be solved in a commercial solver conveniently, such as GUROBI and CPLEX. The validity and practicality of the proposed model have been demonstrated through case studies in several different application scenarios, which also demonstrate the necessity of considering carbon trading mechanisms in the design model

Economic Valuation of Low-Load Operation with Auxiliary Firing of Coal-Fired Units

It is often claimed that coal-fired units are highly inflexible to accommodate variable renewable energy. However, a recently published report illustrates that making existing coal-fired units more flexible is both technically and economically feasible. Auxiliary firing is an effective and promising measure for coal-fired units to reduce their minimum loads and thus augment their flexibility. To implement the economic valuation of low-load operation with auxiliary firing (LLOAF) of coal-fired units, we improve the traditional fuel cost model to express the operating costs of LLOAF and present the economic criterion and economic index to assess the economics of LLOAF for a single coal-fired unit. Moreover, we investigate the economic value of LLOAF in the power system operation via day-ahead unit commitment problem and analyze the impacts on the scheduling results from unit commitment policies and from extra auxiliary fuel costs. Numerical simulations show that with the reduction of the extra auxiliary fuel costs LLOAF of coal-fired units can remarkably decrease the total operating costs of the power system. Some further conclusions are finally drawn

Multi-Flexibility Resources Planning for Power System Considering Carbon Trading

Clean and low-carbon energy represented by wind power and photovoltaic power will develop rapidly and will form a new power system with a high proportion of renewable energy. In the context of a low-carbon economy, how to make reasonable planning for power system flexibility resources is crucial for the development of new power systems. In this paper, we establish a multi-flexibility resource planning model for a power system based on a low carbon economy by considering the planning of multi-flexibility resources of “source–load–storage”. First, a ladder-type carbon trading cost accounting model is proposed, and a set of power system flexibility evaluation indexes are proposed. Then, with the objective of minimizing the sum of low carbon operation cost, investment cost, and operation cost of the system, the planning model of multi-flexibility resources is established by considering constraints such as system power balance constraint, investment constraint, and wind power consumption constraint. Finally, the model proposed in this paper is validated by the IEEE-RTS96 system; the results show that: (1) collaborative planning of source–load–storage multi-flexible resources can obtain the best overall system economics, although the investment cost increases by USD 12.6M, the total system cost is reduced by 11.22% due to the reduction in coal generation consumption cost, carbon trading cost, and wind curtailment penalty cost; (2) as the penetration of wind power grows, the demand for energy storage in the power system is gradually increasing; when the installed capacity of wind power grew from 800 MW to 1600 MW, the demand for new thermal power decreased by 53.5% and the demand for new energy storage increased by 200%; (3) the total cost of the planning model considering ladder-type carbon trading decreases by 1.35% compared to the model without carbon trading, and increases by 2.5% compared to the model considering traditional carbon trading, but its carbon emissions decrease by 5.5%

Optimal Scheduling of a Regional Power System Aiming at Accommodating Clean Energy

The regional power system is an essential mechanism to solve the unbalanced distribution of resources and achieve more efficient resource allocation. In this paper, an optimal scheduling model of the regional power system is developed, to maximize social welfare and minimize clean energy electricity curtailment. This model can realize the optimal allocation of power generation resources and the maximum accommodation of multiple types of clean energy, by minimizing the sum of the electricity purchase cost and the dynamic penalty cost of clean energy. Meanwhile, it considers the modeling of the key AC/DC hybrid tie-line in the regional power grid. To this end, the modeling methods of power transmitted by AC/DC tie-line, the net loss of the tie-line, the stair-like operation of the DC tie-line power, the operation constraints of the DC tie-line are proposed. Then a simulation example study is conducted to verify the effectiveness of the model, which proves that the regional power system can stimulate the resource optimization potential better than the provincial power system