Engaging With Loss Adjusters: Optimizing Your Home Insurance Claim In The Uae

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Engaging With Loss Adjusters: Optimizing Your Home Insurance Claim In The Uae

Engaging With Loss Adjusters: Optimizing Your Home Insurance Claim In The Uae

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Aya Amer Aya Amer Scilit Preprints.org Google Scholar 1, * , Khaled Shaban Khaled Shaban Scilit Preprints.org Google Scholar 2 , Ahmed Gowda Ahmed Gouda Scilit Preprints.org Google Scholar 2 and Ahmed Masoud Ahmed Masoud Scilit Preprints.org Google Scholar 2

Received: 12 November 2020 / Revised: 27 December 2020 / Accepted: 29 December 2020 / Published: 6 January 2021

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This paper proposes a home energy management system (HEMS) that optimizes load demand and distributed energy resources. Optimal demand/generation profiles are presented while considering utility price signals, customer satisfaction, and distribution transformer conditions. Home electricity demand considers electric vehicles (EVs), battery energy storage systems (BESSs), and all types of non-shiftable, switchable, and controllable appliances. Moreover, PV-based renewable energy resources, EVs and BESSs are used as sources of power generated at certain time intervals. In this model, consumers can only perform demand response (DR) actions by contracting with utility operators. A multi-objective demand/generation response is proposed to optimize different load/supply schedules based on pricing plans. The comfort level of customer behavior and a degradation cost reflecting the distribution transformer loss-of-life (LoL) are combined with a multi-objective optimization problem. Simulation results demonstrate the mutual benefits the proposed HEMS provides to customers and utility operators by reducing transformer LoL to reduce power consumption and improve operators’ resources while meeting customers’ comfort needs. The results show that power operating costs and demand peaks are reduced by 31% and 18%, respectively, with transformer LoL % that is reduced by 28% when no DR is applied.

Customer satisfaction; demand response; energy storage systems; electric vehicles; home energy management; loss of life; renewable energy sources; transformer aging; coincident peak

The primary objective of Demand Response (DR) schemes is to match electrical power supply with consumption. Traditionally, utilities adjust generation rates according to changes in demand. This practice is costly as it leads to switching the generation unit on and off, importing power from other utilities or applying load shedding. The advent of smart grids has technically enabled utilities to adjust generation. For example, non-essential loads can be reduced and energy consumption shifted from peak hours to off-peak hours. This is done primarily with customer approval and based on time-based dynamic pricing. Along with these smart grid features, other new and highly invasive elements need to be considered For example, the development of energy-harvesting technologies for energy storage systems (ESSs) and renewable energy sources (RESs) such as photovoltaics (PVs) has led to rapid growth in the integration of PV systems in residential and commercial premises. Furthermore, electric vehicles (EVs) are widely used, especially when grid-to-vehicle (G2V) and vehicle-to-grid (V2G) operations are considered.

Engaging With Loss Adjusters: Optimizing Your Home Insurance Claim In The Uae

DR schemes aim to maximize utility profits by optimal deployment of expensive and critical assets such as transformers, as they affect the adequacy and reliability of the power system and their high utilization efficiency is essential to obtain a reasonable return on investment. Any failure in the transformer can cause significant problems such as power outages and costly and time-consuming repairs and replacements. DR is critical to effectively enhance smart grid performance while considering all these factors and using enabling technologies. A well-designed DR scheme can deliver value beyond what traditional DR models can achieve, such as balancing power supply and demand, optimizing the deployment of utility assets, reducing energy generation costs, reducing dependence on fossil fuels, and accommodating RES. increase [1] ].

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Home energy management systems (HEMSs) are used to control appliances and oversee the energy and data flow of an entire facility [2]. HMES facilitate communication with customers through various channels to ensure their participation in a DR program and to inform them about an event, energy consumption, pricing, etc. Several researchers have proposed HEMS with cost-based DR algorithms to reduce costs This is done by shifting various loads from high price periods to low price periods. A HEMS is presented [3] to shift customer load to reduce power consumption and power peak to average ratio (PAR). To meet consumer power demand, HEMS uses Mixed-Integer Linear Programming (MILP) to determine the on/off status of home appliances on a day-ahead and real-time basis. MILP is also used to reduce power consumption in [4, 5]. These studies only consider household appliances with thermostatically controlled loads and ignore others whose loads can be shifted as well as conventional smart grid appliances such as RESs, EVs and ESSs. A more comprehensive and general optimization-based HEMS is presented in [6]. The model integrates different types of smart equipment including thermal, movable, interruptible and critical equipment. HEMS controls the operation of appliances based on received price signals to reduce the amount of electricity used while lowering the electricity bill. However, the effects of RESs, EVs, and ESSs are not considered in this model.

In [7, 8], the impact of EV charging on residential distribution networks has been investigated. An energy management system is suggested in [7] to optimize the operation of appliances considering plug-in EVs. The authors of [8] proposed a stochastic dynamic programming algorithm of HEMS for plug-in EV charging by incorporating multiple random variables such as EV arrival time, departure time, and energy required for mobility. The results showed that changing mobility patterns influence the optimal decision of HEMS. Recently, several works have been done to integrate RES and ESS into household activities with DR programs. A DR technique for a non-shunted load with PV and ESS capacity is presented in [9]. The aim of the study is to reduce the expected energy consumption by scheduling the charging/discharging time of ESS by considering time-of-use (ToU)-based DR from renewable energy management units. In [10], the authors integrate the charging and discharging methods of ESS and EV batteries in HEMS to prolong their life. The capital cost of the battery is considered for better flexibility and economic benefits. In [11, 12], DR techniques are proposed with the possibility of bi-directional utilization in a single HEMS to reduce the electricity operation cost and improve the electricity load pattern while neglecting the customer comfort.

The work of [13] was devoted to investigating the effect of consumer comfort on the performance of a DR program. In [14], a multi-objective DR scheduling framework for a group of households is proposed. The model takes into account the preferences of participating households and aims to minimize both overall energy production costs and individual electricity bills. Also, a multi-objective HEMS model is presented in [15]. The goal of the HEMS model is to schedule home appliances based on electricity prices and customer satisfaction. The results show that the model was able to reduce electricity consumption for all scenarios used while minimally affecting end consumer satisfaction/comfort. In the study [16], the dissatisfaction model is set as the customer’s energy consumption data for each time slot. Based on actual customer consumption patterns, the user sets up a discomfort model that enables aggressive energy consumption reduction.

DR can be used to reduce the effects of aging by managing the thermal behavior of the transformer. Related to the previous literature, work in the DR literature aims to increase the use of transformers. According to [17], using DR programs can reduce utility investment to install new transformers by 75%. As a strategic solution, DR can reduce the transformer load by shifting and reducing the load of consumers. In [18, 19, 20], the effect of DR on distribution transformer aging has been examined. Various loads were regulated and shifted to reduce loss-of-life (LoL) of the transformer. However, in this study, reducing electricity prices was not considered an objective, nor was customer satisfaction. The work of [21] proposed a DR optimization model based on the transformer hottest-spot temperature.

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