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For large wind-photovoltaic-storage hybrid system, in order to maximize the match between output of the hybrid system and the scheduling plan, it is very important to coordinate the output power of wind, photovoltaic and energy storage system. Taking the uncertain of wind, photovoltaic generation and the power ramp constraint into account, a coordinated optimal control strategy is proposed. The aim of optimization model is to minimize the average expectation of power deviation and charge-discharge times of battery and maximize the average residual capacity of battery. The Non-dominated sorting genetic algorithm (NSGA-II) is used to solve the multi-objective problem for getting the alternative schemes set, and an evaluation method is given to acquire the best scheme. The feasibility of the present strategy is examined by simulation. Introduction The wind-photovoltaic-storage hybrid system, which takes complementary feature between wind and photovoltaic generation into consideration, can reduce the fluctuation of output power and enhance the ability for distributed generators to connect to the grid [1]. Recently, several researches about wind-photovoltaic-storage hybrid system have been published. Literature [2] addressed optimal control of energy storage based on fuzzy correlated-chance programming. The final goal is to maximize the mean value of the total credibility by using the fuzzy simulation. Literature [3] proposed an optimal control of battery energy storage system based on variable smoothing time constant. Literature [4] put forward the sectionalization gradation strategy for battery, which could track the power change of wind power and photovoltaic generation. In this paper, the coordinated day-ahead optimal control strategy is addressed which takes the uncertain of wind, photovoltaic generation and the power ramp constraint into account. The aim of the multi-objective control strategy is to minimize the average math expectation of power deviation and charge-discharge times of battery station and maximize the average residual capacity of battery. By using non-dominated sorting genetic algorithm (NSGA-II) and evaluation method to solve the model, the specific output power strategy is given for wind generators, photovoltaic generators and battery storage individually in each period. Coordinated Optimal Control of Output Power Firstly one day into is divided 24 periods, coordinate the output power of wind, photovoltaic generators and storage system in every period to satisfy the scheduling curve. Choosing the appropriate mathematical model to describe the uncertain of wind power and photovoltaic, establish the objective functions and constraint in mathematical expressions. Then use NSGA-II algorithm and evaluate method to solve the model. Forecast error of wind-photovoltaic in math expression. At present, wind power and photovoltaic have relatively large error in the forecast mainly due to the error from numerical weather International Power, Electronics and Materials Engineering Conference (IPEMEC 2015) © 2015. The authors Published by Atlantis Press 536 forecast, model of output power and prediction method [5]. The forecast error rate could be shown in(1) real forcast forcast P P P e − = (1) Where ε is the forecast error rate, stand for the percentage of forecast error; Preal and Pforcast are the real output and forecast output respectively. According to recent research about error distribution, Beta distribution, Normal distribution, Cauchy distribution and Laplace distribution have been applied to describ the model [6]. Each distribution has it own advantages and scope of application. In this paper, the forecast error rate is defined to obeied to Normal distribution, which shown in(2): 2 2 ( ) 2 2 2 1 ( ; , ) 2 x f x e μ σ μ σ πσ − − = (2) Where μ is the mean or expectation of the distribution and the parameter σ is its standard deviation. Objective function. The main goal of coordinated optimal control is to improve the ability to track scheduling plan and reduce the compensation pressure of battery. Comprehensive analyzing of the above situation, optimization model will help to minimize the average math expectation of power deviation and charge-discharge times of battery station and maximize the average residual capacity of battery station [7]. It is a typical multi-objective problem. (1) Objective function one: Minimize the average expectation of power deviation. The forecast error distribution is obeyed to Normal distribution, and then dividing the probability distribution function into several sections, caculate the integration area of each section. Take equation (3)(4)(5) to get the average expectation of power deviation. -0.25 -0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25 0 1 2 3 4 5 6 Error / % Pr ob ab ili ty d en si ty Fig. 1 Dividing the probability distribution function 1 1 1 min ( ) min ( ) N i f x E i N = = ∑ (3) 1 ( ) [ ( ) ( )] ( , ) M j E i P a j b j e i j e = = < < ⋅ ∑ (4) ( , ) ( , ) ( , ) ( , ) ( ) ( ) / wind pv soc obj obj e i j P i j P i j P i j P i P i   = + + −   (5) Where i is the time period, i=1,2,⋯N; j reprsents the section of probability distribution function, j=1,2,⋯M;e(i,j) reprsents the power deviation in No.i period and No.j section. Pwind(i,j), Psolar(i,j) are the real output of wind generators and photovoltaic generators in No.i period and No.j section, Psoc(i,j) is the charge-discharge power in No.i period and No.j section, Psoc(i,j)>0 means discharge, Psoc(i,j)<0 means charge;Pobj is the schedule output. (2) Objective function two: Minimize the charge-discharge times of battery. The battery storage has three statuses in normal operating state, which are charging, discharging and shutdown. The charge-discharge times will increase one when the status change. 2 min ( ) min b f x N = (6)

Wind-Photovoltaic-Storage Hybrid System Coordinated Optimal Control of Output Power