Complexity

Volume 2018, Article ID 1640395, 11 pages

https://doi.org/10.1155/2018/1640395

## A Novel Control Strategy on Multiple-Mode Application of Electric Vehicle in Distributed Photovoltaic Systems

^{1}School of Urban Railway Transportation, Shanghai University of Engineering Science, Shanghai 201620, China^{2}School of Mechanical Engineering, Donghua University, Shanghai 201620, China

Correspondence should be addressed to Yize Sun; nc.ude.uhd@zynus

Received 21 November 2017; Revised 14 March 2018; Accepted 3 May 2018; Published 11 July 2018

Academic Editor: Tiago Pinto

Copyright © 2018 Qianwen Zhong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

Considering the booming development of electric vehicle (EV), this article presents a novel control scheme analyzing EV multiple-mode application in a number of distributed photovoltaic (PV) systems, which rationalizes the energy flow among the energy system participants containing a power grid, a grid-connected PV system, power consumption devices, storage batteries, and EV. Based on the control scheme, the authors propose two day-ahead optimal control strategies with different objective functions: one is minimizing the daily electricity expense of an individual distributed PV system and the other is minimizing the daily total expense of distributed PV systems which EV can be connected to. The model has been verified by the actual data and forecast data, respectively. The results show under the individual objective, in the distributed PV system with EV, the electricity expense can obtain an annual reduction of 27.18%. Furthermore, in the distributed PV system with a storage battery as well as EV, the electricity expense can obtain an annual reduction from 30.67% to 81.49% with a storage battery capacity changing from 1 kWh to 20 kWh. Under the total objective, the total expense and even the individual expense have different degrees of reduction. However, the specific benefits should be rerationally distributed by balancing the interests of all the distributed PV systems. In addition, besides the application in the distributed PV systems, this model may have some potential on the development of a regional energy system.

#### 1. Introduction

Due to electric vehicles (EVs) in the past several years showing an explosive development, researchers have found that these mobile distributed storage units have great potential in energy systems in future power grids, especially when coordinated with renewable energy. Therefore, the literature on the rational planning, optimal operation of EVs, and renewable energy sources has mushroomed these years. Wu et al. [1] briefly analyze the possible scenarios of using renewable energy to charge EVs. Chen and Duan [2] deal with the daily EV mileage uncertainty by Monte Carlo simulation and design an optimization and integration method of EV in microgrids with minimizing the total cost of electricity as the goal. ElNozahy et al. [3] also use Monte Carlo simulation to provide a probabilistic planning and scheduling method for an energy storage system integrating EVs and photovoltaic (PV) arrays in a distributed power grid. Guo et al. [4] discuss a two-stage renewable energy generation parking lot economy framework for EVs. The first stage processes uncertainty of renewable energy, and the second stage controls EV charging operation based on a predictive model. Considering the smart grid with EV and PV power generation in an islanding operation mode, Tang et al. [5] provide an online reinforcement learning method called object representation adaptive dynamic programming, which is for the adaptive islanding control unit in smart grids. Hashemi et al. [6] present a sensitivity analysis on feasibility of users supplying energy into power grids, to determine the minimum storage system capacity with different positions of low voltage power grid configuration. It prevents the overvoltage caused by PV high penetration, which presents a definition named residual power curve (RPC). Paterakis et al. [7] give a detailed family energy management system structure to determine the best home appliance scheduling strategy based on demand response on the following day when the price changes and power peak limits. Kaschub et al. [8] discuss the impact of different incentives and tariffs on PV storage systems in Germany. Cao [9] compares situations of integrated renewable energy to support the construction of the system with the hydrogen energy vehicles and EVs, respectively, through the reasonable control, which provides a better reference for the implementation of EU’s 2050 line integration of renewable energy vehicles. Kampezidou et al. [10] compare the economic effects of two types of energy storage systems including EVs and pumped storage on high-penetration renewable energy systems. Assuncao et al. [11] present a technical and economic evaluation model for the simulation of EV battery, which is for the mismatch between demand and PV power generation and guidance of economic policy. Marra et al. [12] propose an energy storage strategy to reduce voltage rise of PV feeders by coordinating the load of EVs as an energy storage mode. An intelligent charging and discharging random scheduling method is proposed by Honarmand et al. [13], which is for a large number of EVs in a parking lot. Meanwhile, they design a self-scheduling model considering PV power generation system and distributed generators in the intelligent parking lot. For the traditional industrial microgrid, Derakhshandeh et al. [14] put forward a kind of electricity and thermal power generation scheduling coordination method, which considers the microgrid characteristics of traditional industry, also with the application of EVs, PV systems, and PV energy storage systems. Howlader et al. [15] focus on the optimal operation scheme of the smart grid with conventional thermal generators and distributed generation. To solve the optimal scheduling problem for hybrid energy microgrid including PV, wind power generation, heat and power cogeneration, energy storage systems, and EV, Liu et al. [16] present an optimal scheduling model considering demand response, with minimum total operation cost which includes the cost of natural gas, the cost of power grid and EV charging, and the discharging cost. In the study of Ju et al. [17], wind power, PV power generation, EV, and conventional power plants are combined into a virtual power plant; considering the uncertainty and demand response, they give a two-way stochastic optimal scheduling model for the virtual plant. On that basis, they further improve the original optimization scheduling model [18] by minimizing the cost, minimizing the energy consumption, and maximizing the profit. Similarly, Coelho et al. [19] design a multiobjective power dispatch model to minimize the total cost of the microgrid of EVs, battery, maximum peak load, extreme difference, and double Sharpe ratio index, and the problem is formulated as a mixed-integer linear programming problem. Jaramillo and Weidlich [20] also propose a multiobjective microgrid optimal scheduling model; besides operating costs and peak power costs, environmental indicators are also taken into account. Gao et al. [21] start from the comfort and economy of the home users and divide the load into three categories: fixed, shiftable, and adjustable loads, and then optimize the scheduling of the home energy system according to different kinds of load. Zhao et al. [22] from residential customers’ and public utilities’ views build an integrated demand response simulation optimization framework for high penetration of EVs, PV, and energy storage systems under scenarios of TOU price, real-time price, and curtailment price mechanism. Based on the actual operation of dynamic optimization, Bracco et al. [23] establish an intelligent multipower and sustainable building microgrid test platform with the goal of minimizing cost and CO_{2} emissions at the University of Genova, Savona University Campus, and experiments show that reasonable scheduling optimization is feasible and effective.

The above lists the literature that considers the optimal dispatching control of EVs. However, as the research topic is still in its infancy, the specific criteria have not yet been determined. Most of the current studies are based on large-scale or medium-scale renewable energy power stations, so there are still a lot of problems that need to be solved or improved for distributed PV systems. Besides the above problems, there are few studies considering the multiple modes of EV which contain the application of G2V, V2G, off-grid, and driving modes as well as testing them in multiple locations.

The arrangement of this article is as follows: firstly, in Section 2, the models of each participant in the distributed PV system are illustrated. Secondly, the novel day-ahead control strategies are presented in Section 3 with different objective functions: one is minimizing the daily electricity expense of an individual distributed PV system and the other is minimizing the daily total expense of distributed PV systems which EV can be connected to. Thirdly, to verify the effectiveness of optimal control strategies, the actual data of PV generation and electricity demand are used first. Then, the results are also calculated using forecast data which could be used to discuss the feasibility under forecasting models. Finally, the conclusions are summarized and future work is briefly introduced.

#### 2. Distributed PV System Model

A commonly distributed system involves the power grid, the PV system, and the storage system. In this article, due to the explosive development of EV, the authors take the EV with vehicle-to-grid (V2G) function into consideration. To simplify the model, a storage battery is used as a representative of the storage system. The most important role of batteries equipped in the distributed PV system is through charging or discharging energy to improve the stability and economy of the energy system, and contrary to the EV’s battery, it can be seen as a fixed storage system. The first application of EV battery must satisfy the normal function of EV as a transport. On this basis, it can more be used as an auxiliary storage system participating in the energy adjustment of a distributed PV system. To better distinguish the fixed battery, here, it is seen as a mobile storage system. In the following, a model of each participant in the distributed PV system is introduced or built, which is required in the optimal scheduling control strategy.

##### 2.1. PV Prediction Model

The PV array power output model is selected from [24, 25]: where is the open-circuit voltage, is the short-circuit current, is the voltage at maximum power point, is the current at maximum power point, is the serial number of PV cells in one panel, is the serial number of PV array, is the parallel number of PV array, represents the solar irradiance, represents the temperature, and represents the time interval of the recorded data.

As there must be the difference between the PV model built with parameters from the manufacturer and data recorded in the actual outside environment and the error information of the historical data is missing in this paper, here in this model, the authors assumed that the error of PV power generation forecasting model is consistent with the Gaussian distribution,

In the above equation, represents the error between the PV forecasting value and the actual generation . Through setting the mean error and mean square deviation , the PV power generation forecasting values are randomly generated under different conditions by MATLAB software.

##### 2.2. Electricity Load Forecast Model

A Bayesian neural network (BNN) model is established to forecast the load values with 16 load-related inputs. The vectors of inputs,, are shown:

To build the electricity demand forecast model, the input factors which are mostly considered in the existing models would be the time type and the meteorological type. Based on the similar consideration, in this article, time of every day,; day type (which is defined as integers from 1 to 7 to express Monday to Sunday, resp., and 8 to express special holidays),; ambient temperature, ; and relative humidity, , are firstly considered as the inputs. is used to represent the series order number of historical sample data’s intervals. For instance, means the temperature vector observed from one interval before the interval, which is actually the data from the previous interval. Due to no record before the historical first interval, here is used as the initial value to complement the vector. If other vectors lack some items, the same complement method is used. Besides these vectors, historical load data,, are also used in inputs to increase the accuracy of the forecast model. With a similar meaning of subscript, the eight historical data vectors in the latter half are actual load in the first past interval, the second past interval, the third past interval, the fourth past interval, the same interval of yesterday, the same interval of the day before yesterday, the same interval in last week with the same day type, and the same interval in the week before last week with the same day type, respectively. These eight inputs of the forecast model basically cover the most relevant historical load values within the past two weeks.

##### 2.3. Storage Battery Model

The control algorithm of the storage battery is designed by geometrical-logical control method in another published article of the authors [26]. The key point of this algorithm can be described as follows. Under a time-of-use (TOU) electricity retail tariff, if it satisfies the condition that the tariff is always higher than the feed-in-tariff (FiT) even considering the inefficiencies of energy transformation, ignoring initial capital and maintenance costs of storage battery system, the best size of the battery should equal the sum of the positive values obtained by the load minus the PV generation in every interval within the shoulder and peak time. When the maximum available storage energy of a battery is less than the optimum size but is larger than the surplus PV energy, the controller will fully charge the battery before the peak time. If the available energy of a battery at one day’s beginning is less than the surplus PV energy, the beginning capacity should be used firstly during the morning shoulder shortfall, then the rest should be discharged completely during the morning off-peak time, in order to make the battery empty in preparation for the following period of surplus PV generation.

Besides the above control algorithm of storage batteries, the following (4), (5), and (6) are the assumption or basic limitation on the storage batteries which must be satisfied: where is the energy change of battery. When this parameter is positive, it means the battery has been charged during that period; on the opposite, when it is negative, it means the battery has been discharged during that time. To better distinguish the charging and discharging energy, the authors define two parameters, and , respectively, to represent them, which are limited to greater or equal to 0 and at the same time only one can be greater than 0. If is negative, the battery is discharged and helping to meet the load. Equation (5) gives the limitations of the battery. In order to extend the life of the battery, manufacturers commonly recommend the optimal and the maximum charging and discharging power rates. and represent the maximum charging and discharging rates of the battery, based on manufacturer recommendations. Under this case, the constraints of the battery are two inequalities limiting the bidirectional energy flow of the battery. Equation (6) further limits the battery’s state of charge (SoC) between the lower bound and upper bound if needed. It should be noticed that here the energy change of all the ordered periods from the initial time interval to the current time interval is accumulated because the SoC must satisfy this constraint in every moment during the entire optimization duration. In this equation, is the specified capacity of the storage battery. represents the initial battery energy. and are the minimum and maximum SoC of the battery, respectively. is the energy loss of the battery, mostly resulting from heat loss.

It is worth noting that all the parameters used to explain the values related to direction in the model in this article are nonnegative values. For the following illustration, a parameter named is also given here to represent the maximum energy that could be used in the storage battery when it is fully charged, which is also mentioned and applied in the geometrical logical analysis battery control algorithm.

##### 2.4. EV Battery Model

Similar to the storage battery models, here the following equations of the EV battery can be obtained: where is the energy change of the EV battery. When this parameter is positive, it means the EV battery has been charged during the duration. If this value is negative, the EV battery has been discharged and helping to meet the load. is the energy change in G2V mode. is the energy export in V2G mode. Equation (7) gives the energy usage of the EV battery. and represent the maximum charging and discharging rate of the EV battery, respectively, based on manufacturer recommendations. Equation (9) limits the SoC of the EV battery between the lower bound and upper bound. is the specific size of the EV battery. represents the initial battery energy. and are the minimum and maximum SoC values of the EV battery, respectively. is the energy loss of the EV battery, mostly resulting from heat loss. represents the energy consumed when the EV is driving on a certain trip between two known locations within some time. Subscripts are used to distinguish the different trips between locations if necessary, for example, represents the energy consumption of the EV battery between location and location .

Due to the objective in this article which is to obtain the minimum electricity expenses, the bill reductions should compare with the V2G cost of the EV. In [27], this cost can be calculated by the following:

This equation is used to determine the cost to the EV owner for allowing access to the stored energy in their vehicles, where is the annual cost. is the energy available in each EV per dispatch in kWh. is the number of dispatches per year. is the cost of battery degradation. is the electricity price.

#### 3. Proposed Control Strategies

As the EV has the transport function, it can move from one location to another. In other words, when the EV is connected to the grid of some parking place, its battery can be applied as a small storage unit to that place.

A comprehensive analysis of EVs applied in the distributed PV systems can be shown in Figure 1. The box on the top left shows two scenarios of one PV system, number 1 PV system, in which the left circle is the distributed PV system with EV connected. The basic participants in one distributed PV system include power grid, grid-connected PV generation, storage battery, electricity load, and EV, which would provide or consume electric energy. The top right box illustrates the two probabilities when the EV is out of the number 1 PV system. One is that the EV is in another distributed PV system, the other is that the EV is driving on a trip. The box on the lower left is all the given states of the distributed PV systems when the EV battery is connected to one of them, and the lower right is the state of the distributed PV system when the EV is driving on some trip from one distributed PV system to another or the EV is off-grid.