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An Innovative Heuristic Algorithm for IoT-enabled Smart Homes for Developing Countries
Abstract
This dissertation explores and identifies that home energy management systems
(HEMSs) are used to implement demand side management in homes. Based on integration of renewable energy sources (RESs) and energy storage systems (ESSs),
HEMS operation (HEMO) is classified into demand response (DR) and DR synergized with RESs and ESSs optimal dispatch (DRSREOD). DR-based HEMO depends on shifting of the consumer load towards off-peak times. DRSREOD-based
HEMS benefits the consumer and the utility by reducing the cost of generation,
reducing energy bills, minimizing green house gas (GHG) emissions, achieving
overall energy savings and increasing energy sustainability.
The contributions in this dissertation are three fold. First, this dissertation reviews the most recent literature on various models for DRSREOD-based HEMO.
The reviewed models for HEMO are classified into dichotomous approaches as
DR versus DRSREOD-based individual versus coordinated, deterministic versus
stochastic, single-objective versus multi-objective and conventional techniques versus advanced heuristics-based. In addition, the tradeoffs among the dichotomous
approaches, challenges pertinent to coordination and eminent issues related to
standardization requirements for modeling home appliances (HAs) are investigated.
Second, an improved algorithm for a DRSREOD-based HEMS is then proposed
in this dissertation. This heuristic-based algorithm considers DR, photovoltaic
(PV) availability, the state of charge and charge/discharge rates of the storage
battery and the sharing-based parallel operation of more than one power sources
to supply the required load. The HEMS problem has been solved to minimize the
cost of energy (CE) and time-based discomfort (T BD) with conflicting tradeoffs.
The mixed scheduling of appliances (delayed scheduling for some appliances and
advanced scheduling for others) is introduced to improve the CE and T BD performance parameters using an inclining block rate (IBR) pricing scheme. A set of
optimized tradeoffs between CE and T BD has been computed to address multiobjectivity using a multi-objective genetic algorithm with pareto optimization to
perform the tradeoff analysis and to enable consumers to select the most feasible
solution.
Third, a drastically rising demand of electricity has forced a number of utilities in
developing countries to impose large-scale load shedding (LS). A HEMS based on
DRSREOD integrated with an LS-compensating dispatchable generator (LDG)
(DRSREODLDG) ensures an uninterrupted supply of power for the consumers
subjected to LS. The LDG operation to compensate the interrupted supply of power during the LS hours; however, accompanies the release of GHGs emissions
as well that need to be minimized to conserve the environment. A 3-step simulation
based posteriori method is proposed to develop a scheme for eco-efficient operation
of DRSREODLDG-based HEMS. The method provides the tradeoffs between the
net cost of energy (CEnet) to be paid by the consumer, the T BD due to shifting
of HAs to participate in the HEMS operation and minimal emissions (T EM iss)
from the local LDG.
At step-1, primary tradeoffs for CEnet, T BD and T EM iss are generated through
a heuristic that takes into account PVs availability, the state of charge and the
related rates for the storage system, mixed shifting of HAs, IBR, the sharing-based
parallel operation of power sources, and selling of the renewable energy to the utility. At step-2, a constraint filter based on the average value of T EM iss is used
to filter out the tradeoffs with extremely high values of T EM iss. At step-3, a
constraint filter made up of an average surface fit for T EM iss is applied to screen
out the tradeoffs with marginally high values of T EM iss. The selected solutions
are classified for critical tradeoff analysis to enable the consumer by choosing the
best option from a diverse set of eco-efficient tradeoffs between CEnet, T BD and
T EM iss. Finally, this thesis focuses on decomposed-weighted-sum particle swarm
optimization (DWS-PSO) approach which is proposed for optimal operations of
price-driven DR (PDDR) and PDDR- synergized with the renewable and energy
storage dispatch (PDDR-RED) based HEMSs. Simulation results show the effectiveness of all the proposed schemes in comparison to the previous schemes.
... The rapid increase in energy consumption and rising electricity demands are considered from the main challenges that face many countries in the upcoming years. Since the traditional power grid cannot catch up with this huge demand, smart grids are used to resolve the imbalance between the generated electricity and the users' demand [1]. Smart grid allows two way communication between utilities and users which paved the way for introducing demand side management (DSM). ...
This paper proposes a centralized home energy management (HEM) approach for residential consumers. Two optimization algorithms are proposed with the aim of minimizing total electricity cost and total load deviation while satisfying users’ preferences. A comparison between the performances of the two algorithms is done in terms of peak-to-average ratio (PAR) and total cost. Moreover, optimization algorithm performance is evaluated in case of applying different pricing schemes; time of use (TOU) and quadratic pricing function (QPF).
Extensive research has been conducted in recent years on the development algorithm for optimal utilization of Home Energy Management Systems and the main goal is to determine the optimal applications for rechargeable appliances. This issue applies to Demand Response planning with Renewable Energy Sources and Energy Storage Systems. This thesis will present an improved algorithm for IoT-based HEMS that explores the algorithm, Demand Response, photovoltaic power, charge, and discharge rate of a parallel storage battery using multiple power supplies to charge the case. Consideration will be given to the need for hybrid home appliance planning to improve system performance parameters and to reduce load peaks, including load reduction plans. After conducting the necessary studies, various simulations are performed on the sample systems and the results will be analyzed and evaluated.
Innovative solutions targeting improvements in the behavior of energy consumers will be required to achieve desired efficiency in the use of energy. Among other measures for stimulating consumers’ behavior changes based on attention triggers, personalized recommendations are essential to enhance sustainable progress towards energy efficiency. In light of this challenge, the current study focuses on innovative energy services that are based on intelligent recommendation systems and digital twins. We review several trends associated with the modeling and diffusion of energy services, taking into account the positive interrelationships existing between recommendation provisions and demand-side consumer energy behavior. This is achieved by means of a content analysis of the state-of-the-art works, focusing on the IEEE Xplore and Scopus databases. Based on this review, we present new empirical evidence to validate data-driven twin technologies as novel ways of implementing consumer-oriented demand-side management via sophisticated abstraction of consumers energy behaviors, and identify various barriers associated with the adoption of energy services, especially as they relate to the implementation and overall adoption of the digital-twins concept. Lastly, we use the review to summarize a coherent policy recommendations related to the wide-spread adoption of the digital-twins concept, and demand-side management solutions in general.
Presently, the advancements in the electric system, smart meters, and implementation of renewable energy sources (RES) have yielded extensive changes to the current power grid. This technological innovation in the power grid enhances the generation of electricity to meet the demands of industrial, commercial and residential sectors. However, the industrial sectors are the focus of power grid and its demand-side management (DSM) activities. Neglecting other sectors in the DSM activities can deteriorate the total performance of the power grid. Hence, the notion of DSM and demand response by way of the residential sector makes the smart grid preferable to the current power grid. In this circumstance, this paper proposes a home energy management system (HEMS) that considered the residential sector in DSM activities and the integration of RES and energy storage system (ESS). The proposed HEMS reduces the electricity cost through scheduling of household appliances and ESS in response to the time-of-use (ToU) and critical peak price (CPP) of the electricity market. The proposed HEMS is implemented using the Earliglow based algorithm. For comparative analysis, the simulation results of the proposed method are compared with other methods: Jaya algorithm, enhanced differential evolution and strawberry algorithm. The simulation results of Earliglow based optimization method show that the integration of RES and ESS can provide electricity cost savings up to 62.80% and 20.89% for CPP and ToU. In addition, electricity cost reduction up to 43.25% and 13.83% under the CPP and ToU market prices, respectively.
With the emergence of smart grid (SG), the consumers have the opportunity to integrate renewable energy sources (RESs) and take part in demand side management (DSM). In this paper, we introduce generic home energy management control system (HEMCS) model having energy management control unit (EMCU) to efficiently schedule household load and integrate RESs. The EMCU is based on genetic algorithm (GA), binary particle swarm optimization (BPSO), wind driven optimization (WDO) and our proposed genetic WDO (GWDO) algorithm to schedule appliances of single and multiple homes. For energy pricing, real time pricing (RTP) and inclined block rate (IBR) are combined, because in case of only RTP there is a possibility of building peaks during off peak hours that may damage the entire power system. Moreover, to control demand under the capacity of electricity grid station feasible region is defined and problem is formulated using multiple knapsack (MK). Energy efficient integration of RESs in SG is a challenge due to time varying and intermittent nature of RESs. In this paper, two techniques are proposed: (i) energy storage system (ESS) that smooth out variations in renewable energy generation and (ii) trading of the surplus generation among neighboring consumers. The simulation results show that proposed scheme can avoid voltage rise problem in areas with high penetration of renewable energy and also reduce the electricity cost and peak to average ratio (PAR) of aggregated load.
This paper presents a model for optimal energy management under the time-of-use (ToU) and critical peak price (CPP) market in a microgrid. The microgrid consists of intermittent dispatchable distributed generators, energy storage systems, and multi-home load demands. The optimal energy management problem is a challenging task due to the inherent stochastic behavior of the renewable energy resources. In the past, medium-sized distributed energy resource generation was injected into the main grid with no feasible control mechanism to prevent the waste of power generated by a distributed energy resource which has no control mechanism, especially when the grid power limit is altered. Thus, a Jaya-based optimization method is proposed to shift dispatchable distributed generators within the ToU and CPP scheduling horizon. The proposed model coordinates the power supply of the microgrid components, and trades with the main grid to reduce its fuel costs, production costs, and also maximize the monetary profit from sales revenue. The proposed method is implemented on two microgrid operations: the standalone and grid-connected modes. The simulation results are compared with other optimization methods: enhanced differential evolution (EDE) and strawberry algorithm (SBA). Finally, simulation results show that the Jaya-based optimization method minimizes the fuel cost by up to 38.13%, production cost by up to 93.89%, and yields a monetary benefit of up to 72.78% from sales revenue.
In this paper, we propose a demand side management (DSM) scheme in the residential area for electricity cost and peak to average ratio (PAR) alleviation with maximum users’ satisfaction. For this purpose, we implement state-of-the-art algorithms: enhanced differential evolution (EDE)and teacher learning-based optimization (TLBO). Furthermore, we propose a hybrid technique (HT) having the best features of both aforementioned algorithms. We consider a system model for single smart home as well as for a community (multiple homes) and each home consists of multiple appliances with different priorities. The priority is assigned (to each appliance) by electricity consumers and then the proposed scheme finds an optimal solution according to the assigned priorities. Day-ahead real time pricing (DA-RTP) and critical peak pricing (CPP) are used for electricity cost calculation. To validate our proposed scheme, simulations are carried out and results show that our proposed scheme efficiently achieves the aforementioned objectives. However, when we perform a comparison with existing schemes, HT outperforms other state-of-the-art schemes(TLBO and EDE) in terms of electricity cost and PAR reduction while minimizing the average waiting time.
The energy systems of isolated areas are different from the stable energy systems of mainland areas. The particularity of isolated areas creates more risks for their energy systems planning, and the isolated areas can be considered to be a more specific analysis target than mainland areas. Various energy models and systems have been developed and applied in isolated areas. The major objective of this study is to provide a comprehensive literature review to identify, classify, evaluate and analyze the performance of different methodologies, models and energy systems for isolated areas. Therefore, effective information could be provided to support decision-making toward to appropriate energy models and systems for isolated areas with different scales and demands. This paper reviewed the forecasting techniques of energy demand and renewable energy (RE) resources, energy models, application of hybrid RE systems (HRESs), and management of energy planning for two most representative isolated areas: islands and remote villages. The uncertainty analysis of energy systems of isolated areas is also discussed. It is evident that the indigenous RE resources show great potentials for the energy system of isolated areas, especially the solar and wind resources. The various combinations of photovoltaic (PV), wind, diesel and batteries have been proven more competitive. Also, it is necessary to develop sophisticated models that are more applicable to isolated areas and that consider the distinctive characteristics, practical needs and uncertainties of isolated areas.
Smart grid enables the two-way communication between the suppliers and consumers. Price-driven demand response (PDDR) is one of the important demand response categories that uses price of the energy as control signals to affect consumers’ electricity consumption. The current PDDR programs include critical peak pricing (CPP), time-of-use (TOU) pricing, and real-time pricing. In this paper, we provide a review of the PDDR studies. Detailed evaluations on advantages and disadvantages of each PDDR are provided. Concerns and future research challenges on PDDR are also addressed. It is believed that with the installation of smart meter infrastructures at residential households, price signal can be an efficient market tool for peak demand shaving, risk and reliability management, carbon emission reduction, and energy cost reduction.
Introduction of electric vehicles (EVs) or plug-in electric vehicles (PEVs) in the road transportation can significantly reduce the carbon emission. Hence, the demand of EVs is likely to increase in the near future. Large penetration of EVs will also ultimately result into high loads on the existing power grids. The controlled charging of EVs can have a significant impact on the power grid load, voltage, frequency, and power losses. In this paper, we have provided a comprehensive review of various energy optimization approaches used for EVs charging. Energy optimization approaches used for EVs not only enhance the battery life but also contribute in regulating the voltage and frequency. During EVs charging, various objective functions such as supporting the renewable energy sources, minimization of the peak load, energy cost, and maximization of the aggregator profit have also been studied from optimization perspectives. The controlled and an optimized EVs charging enhances the performance of EVs batteries and conserves the energy in the system by minimizing the load and power losses. The different EVs charging approaches such as centralized and distributed suited for different objective functions have also been studied and compared with respect to various optimization approaches.