**1. Introduction**

Over the past decade, most countries all over the world have taken action towards reducing their polluting emissions by investing in renewable energy sources. Among those sources, particularly, photovoltaic (PV) solar panels and wind power systems have seen a significant growth [1]. However, the increase of renewables goes hand in hand with technical challenges. The stochasticity of both PV and wind power systems causes the maintenance of grid stability to become more di fficult [2,3].

A major stakeholder impacted by the renewable energy transition is the distribution network operator. While end users are becoming increasingly more independent from the grid, the revenue constraint for the grid operator still remains [4]. Under the current tari ff structure, which is primarily based on the energy-volume component, a 'death spiral' phenomenon is imminent [4,5]. Nevertheless, the grid infrastructure costs are mainly dependent on the power capacity of the system. Yet, PV users have reportedly slightly lower peak power than non-PV users [6]. In other words, PV-users pay less than non-PV users even though both of them use the grid almost to the same extent [6]. To counteract such unfairness between di fferent user groups and correctly attribute the costs to their origin, new tari ff structures are being introduced that increase the weight factor for the peak demand component. This (peak demand pricing) will also apply for small user groups such as residential consumers who have been so far excluded from peak power measurements [7,8].

Given these increased peak power costs, peak demand reduction ('peak shaving') has gained much attention in recent years. Peak shaving is not a new concept; industrial users with high peak demand already have been using diesel and gas generators to reduce electricity costs for a long time. Still, those conventional generation methods are expected to be replaced by 'green' technologies, among which energy storage and in particular batteries are the primary candidate.

Battery storage systems have been deployed in the past to provide di fferent types of services, such as (i) increasing the self-su fficiency of PV/wind power installations [9–11], (ii) providing ancillary services to the grid operator [12–14], (iii) peak shaving [15–17], (iv) back-up generators and UPS [18,19]. A common issue, arising particularly in (i), (ii) and (iii), is that due to the high cost of the storage system, battery storage investments are not ye<sup>t</sup> economically feasible. However, we note that in the majority of those studies, the battery is deployed exclusively for one service. Therefore, to accelerate the return of investment, many sugges<sup>t</sup> as a possible solution 'hybridizing' multiple services into a single application instead of providing each one separately [14,20,21]. Before studying how such a hybrid strategy can be applied, we should first identify the technical constraints of the services under consideration. In this paper, we focus specifically on peak shaving and present some insights that reflect its potential for hybridization. In the next paragraph, we review previous research works on peak shaving through battery storage.

In [15], the authors present a sizing methodology for defining the optimal energy and power capacity of battery storage systems used for peak shaving. An economic feasibility study was conducted for two di fferent technologies, lead acid and vanadium redox flow (VRF). A control strategy was proposed, but it assumed that the load profile is perfectly predictable in advance. In [16], the researchers applied peak shaving for residential end users. One of the main conclusions was that the utilization of the lithium-ion battery stays very low, lower than 165 cycles per year. At such a low rate (here, the cycle lifetime is 3000 cycles) the system could be used for more than 20 years unless it exceeded its calendar lifetime. Finally, considering also its calendar lifetime, the battery would have to be replaced approximately after 10–15 years. Furthermore, the researchers suggested adding grid support services next to peak shaving in order to increase the utilization of the system. In [22], the researchers developed a model in Matlab/Simulink where a VRF battery is used to simultaneously provide frequency regulation and peak shaving. It was concluded that the battery storage system can successfully perform both services. However, the experiment was conducted only for a limited time period (30–140 s), thus, in essence, without a ffecting the battery state of charge (SoC) and as a consequence, it was not possible to evaluate the reliability of the control system under unfavorable conditions. In [23], a fuzzy control algorithm was developed for peak shaving in university buildings. The algorithm was tested and compared to two di fferent peak shaving techniques, namely the fixed-threshold and adaptive-threshold controller. The results showed that the proposed algorithm was the best of all. Although the researchers conducted several case studies (with 8 di fferent load profiles), they did not provide su fficient information about the load forecasting method. In [17], a control algorithm is proposed for peak shaving in low-voltage distribution networks based on day ahead aggregated load forecasts. The main novelty of that study is that the algorithm, considering also the inherent forecasting errors, relies solely on historic data; hence there is no need to intervene in real-time and readapt the dis-charging process of the battery. Results from a case study show that peak reduction is achieved for 97% of the time and that for 55% of the time, the peak reduction is at least 10%. In [18,19,24,25], peak shaving is addressed as a secondary application. Here, the primary service of the battery is to provide uninterruptible power supply (UPS) in data centers. The researchers argue that because of the significantly low probability of the peak occurrence (e.g., a Google data center exceeds 90% of its power capacity only for 1% of the time), it is possible to achieve peak reduction without impacting the reliability of the primary service. In [26], a battery sizing methodology and an optimal control algorithm is proposed for peak shaving in industrial and commercial customers. One of the main objectives was to determine an appropriate peak shaving threshold. Three case studies were carried out, each one considering a di fferent daily load profile. The results showed that adapting the peak shaving threshold in real-time leads to higher peak reduction than keeping a fixed threshold based only on a historic data analysis. A drawback of the study might be that when calculating the battery utilization, it is assumed that the battery is equally utilized every weekday of the year, thus omitting possible idle periods on days with low power consumption. In [27], a peak shaving algorithm was proposed for microgrid applications. In contrast to conventional approaches considering only the load consumption, here, the peak threshold applies also for the PV generation. The battery capacity is equally reserved for both positive (injection to the grid) and negative (absorption from the grid) peaks by setting the SoC during normal operation at 50%. The algorithm was tested on a real-time microgrid, implemented in the lab. The researchers used predefined data (load/PV profiles) to carry out the experiment; however, they did sugges<sup>t</sup> in future deploying predictive analytics to improve the reliability of the system.

In this paragraph, we explain three major contribution pillars of the present research work.

	- • How much peak demand reduction can a user achieve for a given battery energy capacity (kWh)?
	- • What is the battery utilization, how much time during the year and how many cycles? Does peak shaving heavily impact the degradation of the battery? Can we hybridize peak shaving with other services?
	- • Which performance metrics should we use and how can these be interpreted from an economic perspective? What are the profitability margins of battery storage for Belgium?

The rest of the paper is structured as follows. In Section 2, the data of the study are presented (Section 2.1). Then, we proceed with the methodology; the power flow model is explained (Section 2.2) and the dichotomy method is proposed as an optimization algorithm (Section 2.3). Section 2 closes with the definition of performance metrics (Section 2.4). Next, Section 3 shows the results of the simulation (Section 3.1) and explains how to interpret those from an economic perspective (Section 3.2). Finally, Section 4 summarizes the most important conclusions and makes suggestions for future research objectives.

## **2. Materials and Methods**
