**1. Introduction**

The release of greenhouse gases, especially CO2, by utilities using coal and gas reduces the ozone layer and creates more pollution. Energy demand in developing countries is projected to rise about 65% by 2040, reflecting the growing prosperity and the accelerating economies. The global energy demand will increase by about 35% due to the world's population growth [1]. The high penetration level of solar photovoltaic (PV) in the utility sector decreases the greenhouse gases emissions and promotes the use of renewable energy compared to conventional energy resources. Solar PV systems are able to deliver an alternative solution to reduce the peaking load throughout the day.

However, the intermittent supply of solar PV system during bad weather condition reduces the ability to supply power during peak hours [2]. Solar PV system in combination with energy storage is expected to be the optimum solution to accommodate the peak load.

In the modern era, energy storage technology has been widely applied for peak load reduction. Energy storage devices such as batteries, thermal storage, and supercapacitors offer similar functionality to peaking power plants. Nevertheless, each of these technologies has economic and technical barriers to be solved [3]. Sustainable Energy Development Authority (SEDA) Malaysia has promoted clean energy use by authorizing the implementation of renewable energy tariff mechanisms under the Renewable Energy Act 2011. In November 2016, the Net Energy Metering (NEM) scheme was implemented to encourage the use of renewable energy (RE), especially solar PV in the grid. NEM permits the self-consumption of generated power by the RE while exporting the surplus power to the utilities at a fixed rate. Under the NEM scheme, the surplus generation rate has been set at MYR 0.31 (USD 0.07)/kWh and MYR 0.238 (USD 0.05)/kWh for low voltage and medium voltage interconnection facilities, respectively [4].

Nevertheless, the low energy rate will reduce the excess energy profit compared to the current Time-of-Use (ToU) pricing scheme of Malaysian electricity tariff [5]. This has driven the focus on optimization of the solar PV-battery system to capitalize on the energy-saving profits associated with the electricity price variances between ToU and NEM scheme. Under the ToU scheme, maximum demand (MD) is measured by recording the peak load over the timeframe of successive 30 min intervals from 8.00 a.m. until 10.00 p.m. every day in a month. Table 1 displays the Malaysian electricity tariff rate for different categories of utility customers. As per Table 1, for industrial and commercial sectors, electricity tariff scheme is effective from 1st January 2014 and supersedes the previous tariff schedule which was effective from 1st June 2011 according to Tenaga Nasional Berhad (TNB), the Malaysian electricity company and only electric utility company in Peninsular Malaysia. Based on the Malaysian electricity scheme under TNB, commercial and industrial sectors are categorized based on different tariff rates. C1 and E1 customers incur flat rate charges for MD and net consumption. For C2 and E2 categories, net consumption will be charged based on peak and off-peak periods together with MD charges. The peak period timeframe is from 8.00 a.m. until 10.00 p.m. and the off-peak period is from 10.00 p.m. until 8.00 a.m., respectively [6]. For these reasons, the commercial and industrial customers are encouraged to manage their load consumption according to their respective electricity tariff scheme by focusing on peak and off-peak period rates. Implementation of renewable energy (RE) projects is expected to reduce the maximum demand and will contribute significantly to the overall generation mix in Malaysia.


**Table 1.** Malaysia electricity tariff categories (Medium Voltage level).

C1 a represents the commercial sector (general) [6]. C2 b represents the commercial sector (peak and off-peak) [6]. E1 c represents the industrial sector (general) [6]. E2 d represents the industrial sector (peak and off-peak) [6].

Adding solar photovoltaic generation to commercial or industrial loads reduces utility energy (kWh) charges, but often has little effect on maximum demand (kW) charges. As per Figure 1, peak demands or maximum demand often occur early in the morning during the beginning of office hours when the solar PV generation is slowly increased. Commercial customers with PV generation may have the same high peak (kW) demand but with a lower average (kW) demand. Since they have a higher maximum demand, they will generally benefit more from maximum demand reduction using a battery energy storage system. Peak shaving or maximum demand reduction is the process of reducing the amount of energy purchased from the utility sector during peak hours. A couple of the options

include reducing consumption by turning o ff non-essential equipment during peak hours. Apart from that, installing solar PV-battery systems that can assist with reducing maximum demand, since much of the peak demand occurs during times when this system would be e ffective.

However, variation in solar irradiance pattern especially during peak hours may lead to a minimal reduction of MD since electricity billing for MD charges are captured on any day with peaking load throughout the month. Therefore, a new approach called Maximum Demand Reduction (MDRed) scheme has been developed to optimally size the solar PV-battery system with respect to Time-of-Use (ToU) pricing scheme of Malaysian electricity tari ff and cost of the overall system corresponding to Return on Investment (ROI). Apart from that, this approach will solve the challenges faced due to intermittency of solar PV generation for reliable operation of maximum demand reduction during peak hours with the support of battery energy storage system.

#### **2. Concept of Maximum Demand Reduction (MDRed) Model**

Recently, a lot of studies and models have been developed using solar PV-battery systems. Braam et al. [7] have developed a novel forecast-based control system for photovoltaic-battery systems. It estimates the photovoltaic excess power and develops a charging plan for the battery to store the energy from the photovoltaic peak-production. Wang et al. [8] have developed a state-space based model for BESS and implement a modest ye<sup>t</sup> e ffective method for peak load reduction by considering device limitations. Pimm et al. [9] have developed a demand model to produce high-resolution domestic load profiles to determine how much peak shaving could be achieved with battery storage. An e fficient technique of finding the prospective peak shaving using electricity storage is developed for this purpose. It shows that adequate levels of storage capacity can provide significant peak demand reductions if properly coordinated. Ru et al. [10] have studied the minimization of the cost associated with purchasing from (or selling back to) the utility grid and the battery capacity loss while at the same time sustaining the load and decreasing the peak demand purchased from the grid.

Apart from that, Linssen et al. [11] have developed the Battery-Photovoltaic-Simulation (BaPSi) model to conduct techno-economic analyses of PV-battery systems. The model reflects the variation in the environment to determine the solar PV sizing and battery storage capacity. For each system, the mixture of the total costs of electric supply as well as associated technical and economic output parameters are calculated. Kleissl et al. [12] have developed an operational battery dispatch control system using linear programming for a solar PV-battery storage system that practices load and solar prediction to alleviate peak load. Moghim et al. [13] developed the battery energy storage system (BESS) control algorithm to concurrently overcome the outage issue and shave the peak demand considering the BESS sizing and degradation, microgrid system cost reduction, as well as microgrid scheduling. Liu et al. [14] studied the energy managemen<sup>t</sup> with battery energy storage system (BESS) optimisation by bearing in mind the cost of distributed generations, cost of battery system, and bi-directional energy trading. Dongol et al. [15] developed the Model Predictive Control (MPC) scheme that could be applied to an existing grid linked household with PV-battery system such that the use of battery is maximized and at the same time, peaks in PV power and load demand are reduced.

Optimal sizing of the solar PV-battery system needs to be based on the e ffectiveness of self-consumption and MD reduction with respect to return on investment. As stated by Subramani, et al. [16], based on Figures 1 and 2, the optimal solar PV-battery sizing depends on important elements such as load pattern, techno-economic traits, and electricity billing scheme to meet high maximum demand reduction. In regard to load profile, the load consumption data of one (1) or two (2) years will be adequate to show the load steadiness mainly on MD level and load consumption pattern during the peak period. Since the MD charges are very high, the energy savings through optimal sizing of the PV and battery system will be able to maximize the electricity bill savings mainly due to peak load reduction for commercial and industrial customers. Battery e fficiency in terms of the state of charge (SOC) and depth of discharge (DOD) together with solar irradiance and PV inverter e fficiency will be key for optimal sizing. The total cost of the solar PV-battery system will be very critical in response to the total energy savings, to have a good return on investment to undertake the project. As the core battery technology matures and unit pricing declines, bi-directional battery converters providing both battery charging (AC to DC) and battery inverting (DC to AC) will emerge as a rapidly growing new market for power converters. The Maximum Demand Reduction (MDRed) model is proposed and developed as an optimization tool for the solar PV-battery system. It focuses on peak load shaving and maximization of self-consumption via PV generation and battery system with respect to MD limits. The MDRed model consist of a solar PV array, PV inverter, lithium-ion battery and bi-directional converter. The MDRed model is developed by considering the technical and economic perspective. Firstly, a technical model is formulated which focuses on the battery storage and solar PV capacity to reduce the net load consumption and maximize the MD shaving for a given load pattern. Secondly, the economic model focuses on the cost associated with the system components compared with the energy savings through net consumption and maximum demand to cater for the highest Return on Investment. A flowchart describing the proposed MDRed model is shown in Figure 2. The required input parameters for MDRed model calculation include the consumer load data, solar PV-battery system specification data, and general economic parameters. In MDRed Model, the electricity supply chain is based on the energy balance between the supply and the demand side with respect to MD limitations.

**Figure 1.** Peak or maximum demand shaving concept using solar PV-battery system [13].

**Figure 2.** Maximum demand reduction (MDRed) modeling approach.

#### **3. MATLAB Genetic Algorithm (GA) for MDRed Optimization Model**

Based on Table 2, optimization techniques such as Genetic Algorithm (GA), Harmony Search (HS), Particle Swarm Optimization (PSO) and Hybrid Optimization are the most prominent algorithms to size the solar PV-battery system.


**Table 2.** Comparison of optimization techniques.

Also, these techniques can deal with the random probability distribution or generate a pattern of renewable energy sources [23]. In this paper, the GA programming is developed for the optimization of optimal solar PV-battery sizing in regard to peak load shaving. GA has several advantages such as problem-solving with numerous solutions, easy to understand and can directly be transferred to existing simulations and models [24]. Therefore, several modeling equations and approaches for designing a solar PV-battery system via GA have been developed to ensure the optimum sizing of the overall system.

The proposed MDRed model using GA coding was developed to solve the optimal capacity of solar PV and battery system to maximize the energy savings via self-consumption and MD shavings. Input data includes hourly solar irradiation in Watts-peak (Wp) and multiple patterns of monthly load power consumption in kilowatts (kW). Besides that, battery charging and discharging shall be operated based on Time of Use (ToU) under Malaysian electricity tari ff for commercial and industrial sector. MDRed modelling scheme is made of mathematical formulation which includes the electricity tari ff model, solar PV model and battery energy storage system model. Apart from that, the economical model mainly focuses on cost of the solar PV and battery system and Return on Investment (ROI).
