Optimization-based Energy Management System to Minimize Electricity Bill for Residential Customer Sancoy Barua Dept. of Electrical and Electronic Emgineering Chittagong University of Engineering and Technology (CUET) Chattogram, Bangladesh sancoy@cuet.ac.bd Nur Mohammad Dept. of Electrical and Electronic Emgineering Chittagong University of Engineering and Technology (CUET) Chattogram, Bangladesh nur.mohammad@cuet.ac.bd Abstract— In order to meet the increasing growth of energy demand, incorporation of renewable resources appears to be a viable solution. With the increase of renewable integration, a big challenge is to handle these intermittent resources in a smart way. An effective approach can be the optimal management of energy of Solar PV panels and energy storage besides the main utility. In this research, an optimization method is employed to manage variable load demand to minimize the overall electricity bill of residential house over a period of 24 hours. Optimization approach schedules the energy resources and decides, when to use grid or cut it off, depending on the availability of solar power, state-of-charge (SOC) of ESS and dynamic electricity tariffs. The impact of using smart energy management system (EMS), is tested by executing three different plausible cases. To verify and validate the real time behaviors of the model, optimization results are compared with the static electricity tariffs and a substantial cost reduction is observed. Keywords— Energy Management System, Solar PV, Energy Storage, State of Charge, Optimization, Dynamic Electricity Tariffs. I. INTRODUCTION Nowadays, the Renewable Energy Resources (RERs) can avail a sustainable development for the existing power systems. RERs are integrated within the system in a distributed process via microgrid schemes. These require the management of optimal technological solutions to carry out information exchange among distributed energy resources (DERs) and the consumers. Energy Management System (EMS) a key ingredient for smart grid can be defined as a control strategy and information exchange system, which provides all necessary functionality for the assurance of supplying energy at minimal operational costs from both generation and distribution ends [1]. Albeit the RERs present many benefits to solve energy scarcity problem, it suffers from the puzzles of intermittent, stochastics and unpredictable generation. Hence, the incorporation of ESS along with RERs can be an attractive solution. The ESS provides the advantage of reducing energy consumption cost by storing surplus energy on high RERs generation period and injecting the stored energy during peak- periods [2]. An outstanding number of research works that implement EMS, develop Optimization algorithm to minimize cost by considering dynamic electricity tariffs and optimal use of energy [3]. The optimization-based simulations adopted in Economic Load Dispatch (ELD), Unit Commitment (UC) and Demand Side Management (DSM) approach of an existing power system, lead to sustainable development [4]‒[6]. A mixed-integer-linear-programming optimization method has been proposed in [7], which solves a deterministic problem to minimize real-time operating cost of a grid-connected PV- Wind-Battery based hybrid Microgrid at the Aalborg University campus. An optimal Home Energy Management System (HEMS) is conceived, using the Rainfall and Salp Swarm Optimization algorithm to define the time-of -use (ToU) prices for the on-peak and shoulder-peak hours [8]. A combination of MIDACO-MATPOWER based optimizer has been introduced in [9] to minimize the power import from grid and maximize the Solar PV utilization. The system formulated an improved self-sufficient EMS for an electric vehicle charging station (EVCS) as an individual load. Reconciling with the flourishing mutability of RERs, a bi-level optimization wind-integrated market clearing model is proposed in [10]. This framework minimizes the operating costs in a day-ahead electricity market, offering least inconvenience for the end-users. Optimization driven Demand Response (DR) and EMS has drawn worldwide attention with emerging communication systems and advanced metering infrastructure (AMI) [11]. These methods provide more realistic DR marketing decisions in terms of monetary value. All of these methods schedule the real-time electricity cost by optimizing energy consumption. However, these methods do not consider the bidirectional information flow between the EMS and ESS, like charging/discharging the ESS based on not only the PV availability but also on the dynamic grid electricity price variation. Therefore, the goal of this paper is to solve that issue by exhibiting a Linear-Programming based Optimization approach to schedule the generation and energy consumption profile of loads over 24-hour time horizon. The remainder of this paper is organized as follows: Section II presents the Microgrid model. Section III provides detail mathematical formulation. Section IV discusses the plausible cases. Results are compared in Section V, followed by concluding remarks at the end in Section VI. II. MICROGRID MODEL The microgrid model in Fig.1 consists of a utility grid of 1.1KV, which is supplying a residential load of 440V at 0.95 lagging power factor. Overall power requirement of the variable 2021 3rd International Conference on Electrical & Electronic Engineering (ICEEE) | 978-1-6654-8281-3/21/$31.00 ©2021 IEEE | DOI: 10.1109/ICEEE54059.2021.9718575 978-1-6654-8281-3/21/$31.00 ©2021 IEEE 105 3rd International Conference on Electrical & Electronic Engineering (ICEEE),22-24 December, 2021, EEE, RUET, Bangladesh Authorized licensed use limited to: Chittagon University of Engineering and Technology. Downloaded on March 30,2022 at 10:14:42 UTC from IEEE Xplore. Restrictions apply.