Elsevier

Renewable Energy

Volume 133, April 2019, Pages 442-461
Renewable Energy

Impacts of stochastic forecast errors of renewable energy generation and load demands on microgrid operation

https://doi.org/10.1016/j.renene.2018.09.110Get rights and content

Highlights

  • Adverse impacts of SFE on the operation stability of ESS are investigated.

  • SFE propagation and accumulation models are introduced.

  • Cumulative impacts of SFE and uncertain factors on the variance of ESS SOC are quantified.

  • Impacts of SFE on service lifetime of MT, cycle life of ESS, and operation economy of MG are examined and evaluated.

  • A new simulation program MG-ROS is developed to simulate the actual operation of MG under SFE.

Abstract

Evaluating the impacts related to stochastic forecast errors (SFE) of renewable energy generation and load demands on the operation of micro grid is an important issue. In previous researches, the negative effects of SFE on micro grid are mainly focusing on power quality, system control, and operation, etc. The objective of this study is to investigate how SFE affects the operation stability of energy storage system (ESS) from the perspective of the state of charge (SOC). To this end, novel SFE propagation and accumulation models are introduced. Efficient quantification models of impacts of SFE on the variance of SOC are presented. These models elucidate in detail the mechanism of ESS working from stable to unstable state due to SOC deviation. Then, some metrics are introduced to evaluate the impacts of SFE on service lifetime of units, the cycle life of ESS and operation economy of micro grid. Finally, illustrative numerical results are provided and compared using a self-developed new simulation program named MG-ROS, which can simulate the actual operation of micro grid under SFE.

Introduction

MICRO GRID (MG) is a small-scale power system comprising a variety of controllable distributed generators (DGs), like micro gas turbine (MT), diesel generator (DG) and energy storage system (ESS). Non-controllable DGs, like photovoltaic (PV) cells and wind turbine (WT) generators, are also usually installed in MG to promote the utilization of renewable energy sources (RESs).

To realize the long-term operation stability and economy of MG, optimal coordination between DGs is essential. Ideally, MG will be scheduled in such a way that the objective value of total operating cost is minimized given the operation constraints upon the system while the valid service life of ESS is maximized [1,2]. And typically the optimal operation schedule is formulated by energy management system (EMS) by utilizing the day-ahead forecast values of RESs power and load demands [3,4].

However, the output power of RESs has high random, volatile and fluctuation characteristics. The uncertainty of RESs power generation has posed new challenges to MG. Despite recent improvements in forecasting technology, the accuracy of the forecast for RESs power is not high enough [5]. Stochastic forecast errors (SFE), namely the error between forecast value and actual value, always exist [[6], [7], [8], [9], [10]]. For high RESs penetrations, the importance of investigating impacts of SFE on MG is well-recognized. The inherent volatility of RESs power and uncertainty associated with SFE would impact upon the operation performance of MG from numerous aspects and hamper the efficient utilization of RESs [11]. For instance, increasing voltage fluctuation, flicker, harmonic, and reducing power quality [12]; increasing frequency fluctuation [13]; lowering system reliability [14], safety, transient and static stability [15]; causing reserve power inadequate and hardening the determination of optimal spinning reserve capacity [16]; complicating power market operation [17,18] and system operation [19]; reducing the effectiveness of economic programming [20,21], economic dispatch [22] and unit commitment [23]; rising greenhouse gas and pollutant discharge [24] and electricity price [25]; hardening the control of optimal power flow [26], active and reactive power flow by UPFC equipment [27], and exchange power at PCC [28]; resulting in difficulty of peak load regulation [29]; widening peak and valley difference [30]; augmenting the loss of network [31], the variance of net load [32], the probability of power supply-demand unbalance [33] and the loss of load [34]; abating the operating efficiency and service life of MT [35] and total service life and economy of ESS [36].

To cope with SFE and uncertainties which are caused by the increasing penetration of RESs power, considerable EMS strategies and control methods have been applied to MG operation, such as stochastic (dynamic) programming [8,18], stochastic unit commitment and economic dispatch [37,38], chance constraint [39], security constraint [2,40], probability distribution [5,40], robust optimization and stochastic optimization [37,40], controllable loads [8] and load shifting [9].

In MG, droop control method is adopted by AGC units (MT/DG and ESS). Meanwhile, day-ahead schedule command is utilized as the droop curve power set point, and reserve margins are universally imposed [41,42]. The purpose of doing this is to provide flexibility and implicitly accommodate the uncertainty of forecasts, by instantaneously rebalancing power generation and load demands, and by ensuring there is sufficient generation capacity available to meet unexpectedly power fluctuation. This would result in the deviation between actual power and day-ahead schedule power of MT/DG and ESS after SFE is compensated by AGC units [1,3,41]. Frequency deviation is also produced. After frequency is restored by secondary frequency control, the power error is even greater. For the reason that the operation of ESS is carried out in strict accordance with the day-ahead schedule, and the capacity of ESS is fixed and the coordination timescale between day-ahead and AGC differs considerably. The effect of energy error induced by the product of power error and time cannot be neglected. SOC error of ESS is therefore generated and would increase with operation time by iterative calculation. Actually, SOC value is a critical factor to determine the operation status of ESS. SOC error accumulation continuously would lead to actual SOC exceeding its normal range, as a result of which, ESS is forced to limit its power or even be off-the-line by battery management system (BMS) to avoid overcharge or over discharge. Therefore power regulation capability of ESS is declined and consequently, long-term operation stability of ESS and MG is reduced.

Despite recent advances and the fact that the negative effects of SFE on power system have been extensively investigated in the literature above, the adverse impacts of SFE and uncertain factors on ESS SOC, and leading to the abnormal operation of ESS, have not yet been investigated. Besides, although numerous EMS and control strategies mentioned above have good robustness in dealing with disturbance and uncertainty, none of them solved the problem of ESS SOC deviation when the operation of MG exist SFE. To solve this, accurate and efficient representations of SFE and uncertainties on ESS are essential for the success of the long-term stable operation of ESS and MG.

This paper examines the impacts of SFE on the performance of ESS from the distinctive perspective of SOC in order to understand the mechanism of ESS working from stable to unstable state due to SOC deviation. The SFE propagation and accumulation models are presented and the adverse impacts of SFE and stochastic factors on the variance of SOC are quantified. Quantizing the effects and modeling the uncertainty explicitly can provide researchers helpful guidance on hedging against uncertainties and facilitate them to put forward more pertinent strategies to cope with SFE, such that reliance on reserve margins can be reduced and unstable operation of ESS can be prevented. Handling SFE rationally is of great significance for MG to improve the operation stability and facilitate further improvement of the penetration of renewable energy. Therefore this research is meaningful. In addition, evaluation of impacts of SFE on the quality of optimization results of EMS, on valid service lifetime of MT and cycle life of ESS, on energy price and on operation economy of MG has also been studied.

A new MG real operation simulation (MG-ROS) program is developed to simulate the actual operation of MG and analyze the cumulative impacts of increasing quantities of SFE and uncertain factors upon SOC of ESS and operation stability of ESS. This program has also been used to assess the impacts of SFE on operation economy of MG.

The main contributions of this paper are as follows.

  • 1)

    The random and stochastic characteristics of SFE are modeled and investigated.

  • 2)

    The mechanism of ESS working from stable to unstable state owing to SOC deviation is elucidated.

  • 3)

    Novel SFE propagation and accumulation models are introduced.

  • 4)

    Adverse impacts of SFE and uncertain factors on the variance of ESS SOC and operation economy of MG are quantified.

  • 5)

    Negative impacts of SFE on the total valid service life of MT, cycle times of ESS and energy price are investigated.

  • 6)

    A new simulation method and program MG-ROS is developed to simulate the actual operation of MG considering SFE.

The remaining sections are outlined as follows. Section 2 provides EMS optimization model of MG. In section 3 the mathematic modeling of SFE is presented. Section 4 presents the propagation, accumulation and quantification models of SFE. In section 5 numerical results of MG simulated operation in MG-ROS are presented and discussed. In section 6, conclusions are drawn.

Section snippets

Day-ahead optimal optimization model

Typically, day-ahead forecasts for WT, PV power generation and load power demands are utilized by EMS to generate day-ahead operation schedule of MG, as is shown in Fig. 1. Day-ahead EMS optimization model is formulated as a large-scale mixed integer nonlinear programming (MINLP) problem. And generally, the objective function, a set of equality and inequality operation constraints, integer and binary optimization variables are included in this model. Dynamic programming (DP), heuristic

Modeling of stochastic forecast errors of renewable energy generation and load demands

The day-ahead operation schedule of MG which is formulated by EMS using day-ahead forecast values of RESs power and load demands is globally optimal. However, the power of RESs and load demands have high intermittency, volatility and randomness characteristics. SFE would cause the actual operation of MG in real-time to become non-optimal [6,7]. In general, the smaller SFE is, the less impact it will has on the operation of MG. In this section, probability density function (PDF) is utilized to

Quantification of impacts of SFE on the operation of MG

In this section, propagation and accumulation models of SFE of net load are elaborated in detail according to error propagation theory. The effects of SFE on the optimization results of EMS, on the operation stability of ESS from the perspective of SOC variance and on the economy of MG are analyzed and quantified.

Simulation setup

A simplified MG system is used for testing and analyzing the impacts of SFE. The system contains one PV array, one WT generator, one MT, one ESS with the capacity of 150 kWh (3000 kW·3min) and several loads. The operation parameters are given in Table 1 and Table 2. In order to investigate and evaluate the degree of the impacts of SFE on SOC of ESS and on the economy of MG, four types of operation setups are considered. The first setup is the base case, where no SFE is taken into consideration.

Conclusion

In this paper, the impacts of SFE of RESs power generation and load demands upon MG for a high RESs penetration system are examined. The mechanism of ESS operating from stable to unstable state due to SOC deviation is elucidated in detail. Novel SFE propagation and accumulation models are introduced. Moreover, the cumulative impacts of SFE on the variance of SOC are quantified. Besides, comprehensive evaluations of impacts of SFE on operation economy of MG are carried out. Some conclusions are

Acknowledgement

This work was supported by The National Key Research and Development Program of China (2017YFB0903705). Changhong Deng is currently a Professor of the School of Electrical Engineering, Wuhan University, Wuhan 430072, Hubei Province, China.

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