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An Incentive-based Optimal Energy Consumption Scheduling Algorithm for Residential Users


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Smart Grid is the most promising concept which is more reliable, flexible, controllable and environment friendly. Home energy management (HEM) system is an important part of the smart grid that provides a number of benefits to the end users such as savings in the electricity bill, reduction in peak demand and meeting the demand side requirements. Demand Response (DR) and Time-of-Use (ToU) pricing refer to programs which offer incentives to the end users who curtail their energy use during times of peak demand. This paper proposes an energy efficient optimization model based on Binary Particle Swarm Optimization (BPSO) for residential electricity consumers. The proposed model optimally schedules the electricity consumption of different household appliances in a dynamic pricing environment to benefice the user by minimizing electricity cost. Simulation results illustrate that the proposed method efficiently shifts the appliances operation time from high peak to low peak hours and leads to significant electricity bill saving.
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Procedia Computer Science 00 (2015) 000–000
6th International Conference on Ambient Systems, Networks and Technologies, ANT 2015 and
the 5th International Conference on Sustainable Energy Information Technology, SEIT 2015
An Incentive-based Optimal Energy Consumption
Scheduling Algorithm for Residential Users
Ihsan Ullaha, Nadeem Javaidb,
, Zahoor A. Khanc,
Umar Qasimd, Zafar A. Khane, Sahibzada A. Mehmooda
aUniversity of Engineering &Technology Peshawar, Pakistan
bCOMSATS Institute of Information Technology, Islamabad, Pakistan
cCIS, Higher Colleges of Technology, Fujairah Campus, UAE
dUniversity of Alberta, Alberta, Canada
eMirpur University of Science and Technology, Mirpur Azad Kashmir, Pakistan
Smart Grid is the most promising concept which is more reliable, flexible, controllable and environment friendly. Home energy
management (HEM) system is an important part of the smart grid that provides a number of benefits to the end users such as
savings in the electricity bill, reduction in peak demand and meeting the demand side requirements. Demand Response (DR) and
Time-of-Use (ToU) pricing refer to programs which oer incentives to the end users who curtail their energy use during times of
peak demand. This paper proposes an energy ecient optimization model based on Binary Particle Swarm Optimization (BPSO)
for residential electricity consumers. The proposed model optimally schedules the electricity consumption of dierent household
appliances in a dynamic pricing environment to benefice the user by minimizing electricity cost. Simulation results illustrate that
the proposed method eciently shifts the appliances operation time from high peak to low peak hours and leads to significant
electricity bill saving.
2015 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of the Conference Program Chairs.
Keywords: Appliance scheduling; Binary Particle Swarm optimization; Energy management System; Electricity Pricing; Smart Grid.
1. Introduction
Future smart grid has been considered as an intelligent electricity generation, transmission and delivery system
equipped with an advanced information and control technologies. It aims at improving the eciency and reliability
of the grid, and relieving economic and environmental issues caused by the traditional fossil-fueled generation [1].
Global energy demand increases steadily each year while the growth of electrical energy generation and transmission
setups increases at a much slower rate. Therefore currently, increasing the total generation capacity is a function of
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2015 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of the Conference Program Chairs.
2Author name /Procedia Computer Science 00 (2015) 000–000
peak demand. Ecient supply-demand management and great exploitation of renewable energy are two important
features of a smart grid. Due to its limited capability in information-exchange, the traditional grid suers from
inecient operations at both the supply-side and the demand-side [2].
According to the U.S consumers electricity data report, household appliances consume about 42% of the residential
energy [3]. Energy optimization is one of the active topic and one of the major challenge that needs a proper attention
this time. Dierent optimization approaches, models and schemes for better energy management and consumption
is proposed and deployed. New devices based on new technologies are being deployed e.g., controllable household
appliances, advanced smart meters, stand-alone electrical energy generation and storage systems, i.e., plug-in hybrid
electric vehicle batteries and a communication infrastructure of high potentials. The shift in the energy consumption
pattern of the household appliances form high peak hours to low peak hours is said to be Demand Response (DR).
The shift causes an increase in the energy demand at that specific time horizon [4], [5].
Price based demand response programs consider flattening demand fluctuations as their ultimate objective. Both
the customer and the utility will benefit from DR [6]. A DR strategy coordinates the requirements and needs between
the energy provider and the customer [7]. The end users that take part in the DR program get benefits from it, but
besides it DR program is also beneficial for the utility grid. The DR program provides benefits to the utility grid by
protecting it from dierent risk factors like blackouts, thus increasing the smart grid reliability. It also reduces the
peak demand at certain time horizon, thus reduce the need of the expensive generation plants [8].
2. Related Work
Recently, residential energy management has become an active topic with respect to research and also has a need
of an implementation on the real test bed. In EM system, appliance scheduling is one of the main and important
parameter that needs proper attention and therefore, several appliance scheduling strategies have been proposed by
dierent researchers. Smart grid is a network of technologies that provides electricity from power plants to the end
user and connects all supply, grid and demand elements via an eective communication system.
In [9], EM system is deployed in a home to schedule the electricity consumption in such a way that PAR and
electricity Cost is reduced to the maximum extent. An optimization approach based on RTP combined with the IBR
pricing scheme is used for the power consumption of all the Automatically Operated Appliances (AOA) in the home.
The objective of the DSM strategy is to increase the use of renewable energy resources, increase the economic
benefit and reduce the power imported from the main distribution grid or minimize the peak load demand [10].
According to the proposed architecture, the objective load curve is taken as an input by the DSM system and demands
for the control action in order to meet the desired load consumption.
A Genetic Algorithm (GA) based optimization approach combined with a two point estimate method is used to
meet the Heating Ventilation and Air conditioning (HVAC) load with a hybrid renewable energy generation and energy
storage system. Hybrid generation systems are inherently unpredictable because of the intermittent nature of the wind,
solar irradiance [11].
Residential users are not much aware about the importance of DR program and most of them have not tools for
taking part in DR. Since, residential customers have not resources, a few residential users take part in a DR program.
Commercial and industrial customers have tools and therefore widely participate in the DR programs [12].
Distributed resources are used to describe mainly three new concepts, i.e., DR, distributed generation and elec-
tricity storage. These distributed resources are connected in low and medium voltage level inside of the grid. This
connection of distributed resources inside of the grid represents a radical change for the operation [13]. An oppor-
tunistic scheduling scheme is proposed based on the optimal stopping rule for smart appliance automation control
[13], [3].
The rest of the paper is organized as follows. The motivation of the paper is discussed in section III. In section
IV, the system model is discussed and the problem is formulated as an optimization problem. Simulation results are
presented in section V to show the performance and comparison with traditional users and smart users. Finally we
concluded in section VI.
Author name /Procedia Computer Science 00 (2015) 000–000 3
3. Motivation
Smart grid gives opportunity to the end users to communicate bi-directional with the utility in real-time, so con-
sumers can tailor their energy consumption based on individual preferences like price concern, etc. Based on the
dierent usage pattern of the electrical devices, the Smart Grid oers dynamic pricing scheme in order to avoid dif-
ferent risk factors like a blackout or load shedding, thus allows the user to curtail the energy consumption during high
peak hours. Mostly, the users are not aware of the DR program and also have no tools to take part in a DR program,
thus may not consume the energy optimally and pay maximum cost. Based on the dynamic pricing, there is therefore a
need to develop smart systems that will autonomously execute all these tasks without the prompting of the customers.
In this paper, we proposed a demand side energy management model for a household that is connected to the grid
and also generate some energy from RES. The model is ecient, smart, robust and is capable of coping with the
price uncertainty and maximizes the utility of the customers by consuming energy optimally and benefices the user
by paying minimum electricity cost. In this model, the SS receives the dynamic price signal from the grid and adjusts
the hourly load level of the user in response to hourly electricity prices. The SS firstly, schedules the smart appliances
by shifting the maximum allowable load from high peak hours to low peak hours. Secondly, the SS checks the hourly
energy cost and switches the load from the smart grid to the RES storage where the load costs maximum.
4. Proposed Approach to optimize the energy consumption
In this section, an optimal approach for scheduling the power usage of smart appliances in home is proposed based
on the ToU pricing scheme. In this work, we present a new and an innovative model to anticipate the electricity
usage pattern for residential electrical appliances. The HEM system is equipped with an intelligent Smart Schedular
(SS) and all the household appliances bi-directionally communicate with the SS. The home has rooftop Renewable
Energy Source (RES) generation and storage system. In this model, the SS finds the operation pattern for the smart
appliances in the search space and decides the optimal time for the smart appliances in order to benefice the resident
by minimizing the electricity cost. The SS optimally utilizes the utility grid energy as well as RES stored energy.
During low peak hours, the SS utilizes the grid energy and shifts the load from grid to the RES system when the grid
energy costs maximum to the user. The simulation results reveal that the presented appliance scheduling scheme oer
benefits to the household and generally enjoy lower bills as compared to the users having no HEM architecture in
their homes. Moreover, the energy consumers are categorized based on the scheduling pattern of the appliances, i.e.,
i) Traditional users- this class of user is taken into account without HEM. This type of users are non-price sensitive,
ii) Smart users- this class of user is taken into account with HEM architecture, and iii) Smart Prosumers- this type
of users not only consume the grid energy but also produce some energy from RES. This class of users has HEM
architecture and RES both in their homes.
An optimal approach for scheduling the power usage of smart appliances in home is proposed based on the ToU
pricing scheme. An algorithm based on BPSO technique is used to anticipate the optimal time for making the appli-
ances to operate. All the home appliances communicate with the central control unit, i.e., SS at each time horizon h.
The SS based on the BPSO calculates and generates the optimal time pattern for all the household appliances. The SS
checks the 24 hour time horizon and take decisions based on that optimal generated pattern to operate the appliances
in order to complete the task.
4.1. Problem Formulation for Appliance Scheduling
This section presents how BPSO can solve the appliance scheduling problem. First, we define the optimization
problem following the modified implementation of the BPSO algorithm, which in combination with RES cognition,
provide promising performance.
4.2. The Optimization Problem
It is of great importance to distribute loads properly in the Hhour horizon, such that one can get the maximum
profit out of the smart in-home system. Thus, we define the optimization problem as follows:
4Author name /Procedia Computer Science 00 (2015) 000–000
Fig. 1: Appliance power rating
Given a set of appliances, i.e., A={a1,a2, ..., aN}, where each appliance consumes dierent energy and their
consumption rating is shown in fig. 1. Such appliances are connected to the SS of the HEM. The cost of electricity in
the smart grid is based on the time, i.e., dierent prices are set by the utility for dierent time horizon over a day. We
assumed four levels of ToU, namely high peak cost, shoulder peak, low peak cost, opeak, and is tabulated in table.1.
It is common that during high peak and low peak hours the electricity price is relatively high and low, respectively.
Noticeably, each user uhas one goal, to optimally consume the energy by paying the minimum electricity cost.
The overall objective function is to minimize its electricity bill payment and is hence formulated as follows:
subject to :
Eh,aEgrid (2)
1h24 (3)
where Cis the cost of energy and Eh,ais the amount of energy consumed by the appliance aduring time horizon h.
Particle swarm optimization is a heuristic population based search technique that locates the solution to an opti-
mization problem. Optimization involves binary-valued so Binary Particle Swarm Optimization (BPSO) technique is
used to find the best fitness value for the objective function. Particles represent candidate solutions in a solution space
and the optimal solution is found through moving the particles in the D-dimensional solution space. The particles
initial positions and velocities are randomly initialized. Nparticles combine together to form a swarm. Afterwards,
they move around the solution space to find the optimal solution. The global best at the end of the simulation is taken
as the solution to the problem. The fitness of all particles are evaluated and the global and personal best positions are
updated if needed. Each particle then fly in the search space and each particle dynamically update their position and
velocity by tracking two extremes, i.e., Plbest and Pgbest in each iteration.
5. Results and Discussions
To evaluate the performance of the proposed appliance schemes, we simulated the daily energy use of a set of
household appliances. The attributes, i.e., number of appliances and the power rating of the appliances were set as
shown in fig. 1. Simulations are performed for three main cases i.e., i) traditional user, ii) smart user and iii) smart
Author name /Procedia Computer Science 00 (2015) 000–000 5
prosumer. This study assumes that a household PhotoVoltaic (PV) generation must be able to meet at least 30% of its
load demand. The ToU pricing policy is used for billing of the energy users. These prices are typically established
Table 1: ToU pricing scheme
High Peak hours Shoulder peak hours Low peak hours Opeak hours
1am-4am,7pm-9pm 9am-2pm 5am-8am,10pm-12am 3pm-6pm
well in advance by the utility grid. The dierential or ToU pricing provides financial incentives to the customers who
take part in DR program for shifting their load from high peak to opeak periods. In dierential pricing, the cost of
electricity is charged at dierent rates during dierent time horizons of the day and is tabulated in table.1. In order
0 5 10 15 20 25
Energy consumption (KWh)
without HEM
(a) Unscheduled load energy consumption.
0 5 10 15 20 25
Time (hours)
Cost (Rs)
without HEM
(b) Unscheduled load energy cost.
Fig. 2: Traditional User profile.
to demonstrate the eectiveness of our dierent designed appliance scheduling schemes, the simulation results and
comparison of all the three cases and their performance are analyzed and discussed in this section.
The dierential time pricing policy is used for billing of the traditional energy users. They, therefore wholly rely
on the utility grid to meet the power demands of their electrical devices. The energy obtained from the grid and is
consumed by dierent appliances in dierent time horizon is shown in fig. 2a and the energy cost for the unscheduled
load is shown in fig. 2b. In the second case, the home has smart appliances and HEM system is improved with an
intelligent Smart Scheduler (SS), so called smart homes. These are the customers who have no RES and therefore
wholly rely on the utility grid to meet the power demands of their electrical appliances. The daily energy consumption
profile of the smart home is shown in fig. 3a.
The SS eciently responds against the utility tariand avoids the appliances to operate during high peak hours
and thus benefices the user to pay minimum electricity bill. For scheduled load, a plot of daily cost is demonstrated
in fig. 3b. It is evident from fig. 3b that the SS shifts the appliances pattern from high peak hours to shoulder, low
and opeak hours which results in minimizing the end user electricity bill. The energy consumed by the appliances
in smart homes in comparison with unscheduled load is demonstrated in fig. 4a. It is evident from fig.4a that the SS
shifts the appliances eciently from high peak hours to low peak hours. The daily energy cost of both the smart user
and the traditional user is shown in fig. 4b.
In the third scenario, the users have a smart appliances scheduler as well as RES and storage system. Such user
daily generates 30% of the energy of its total daily load. The performance of the algorithm to optimally consume the
6Author name /Procedia Computer Science 00 (2015) 000–000
0 5 10 15 20 25
Energy consumption (KWh)
with HEM
(a) Energy consumption.
0 5 10 15 20 25
Electricity Cost (Rs)
with HEM
(b) Scheduled load energy cost.
Fig. 3: Smart User profile.
0 5 10 15 20 25
Energy consumption (KWh)
with HEM
without HEM
(a) Unscheduled and scheduled load energy consumption.
0 5 10 15 20 25
Cost (Rs)
with HEM
without HEM
(b) Unscheduled and scheduled load energy cost.
Fig. 4: Comparison of Traditional and Smart user.
grid energy as well as the RES is shown in fig. 5a. The SS utilizes the RES stored energy and shifts the load from the
grid to RES stored energy and thus minimizes the electricity cost by a very significant amount. The energy cost of the
SS with PV generation is shown in fig. 5b. From the fig. 5a, it is clear that the SS optimally schedules the appliances
where EP is minimum and shifts the maximum possible load to RES storage system during high peak cost. In this
way the resident exploits optimally the RES stored energy during high peak cost and eliminates the high peaks in the
electricity cost. The cost profile of the HEM enabled home is quite minimum as compared to the traditional users. The
energy cost is Rs.1404.5 for the home without HEM, the cost is Rs.1132.5 for the home with HEM which accounts
for about 19.36% of reduction in the smart user bill.
Finally, a relative comparison of the three cases is taken into account. The energy consumed by the three cases and
the cost against these consumptions is shown in fig. 5a and fig. 5b, respectively. The smart prosumer gets benefits of
the ToU program and thus optimally exploits the grid energy and residential energy as well. For the home, having
HEM with/without RES, total daily energy costs are Rs.804 and Rs.1132.5, respectively. The reduction in energy
consumption cost is approximately 29% in that case. From the simulation results, it is clear that the proposed algorithm
incites the prosumer by 43% every day with respect to traditional user.
Author name /Procedia Computer Science 00 (2015) 000–000 7
0 5 10 15 20 25
Time (hours)
Energy consumption (KWh)
HEM with RES
HEM without RES
Without HEM
(a) Energy consumption profile.
0 5 10 15 20
Time (hours)
Cost (Rs)
HEM with RES
HEM without RES
Without HEM
(b) Energy cost profile.
Fig. 5: Daily energy consumption and cost profile of the three users.
6. Conclusion
In this paper, a new appliance scheduling model based on BPSO is proposed. The proposed model based on a ToU
pricing scheme eciently schedules the household appliances and benefices the end user by minimizing the daily
electricity cost. The results obtained from several case studies, including HEM and RES revealed that the model
eciently schedule the household appliances and cost saving is achieved in the user electricity bill. Simulation results
show that the proposed model and appliance scheduling algorithm reduce the bill of prosumer by more than 29% with
respect to smart users. Moreover, when the house is supplied from the grid only, the HEM architecture proposed in
this paper still reduce the bill by 19.36%.
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... Home energy management is a device used to monitor the electrical usage of appliances within home (Ullah et al., 2015). By using energy efficient optimisation model, electricity can be scheduled for optimised use using binary particle swarm optimisation (BPSO) (Ullah et al., 2015). ...
... Home energy management is a device used to monitor the electrical usage of appliances within home (Ullah et al., 2015). By using energy efficient optimisation model, electricity can be scheduled for optimised use using binary particle swarm optimisation (BPSO) (Ullah et al., 2015). However, further research undertaken by Khan et al. (2018) found that the improved reliability and efficiency of the system can result in a larger PAR. ...
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Purpose There are 29 million homes in the UK, accounting for 14% of the UK's energy consumption. This is given that UK has one of the highest water and energy demands in Europe which needs to be addressed according to the Committee on Climate Change (CCC). Smart homes technology holds a current perception that it is principally used by “tech-savvy” users with larger budgets. However, smart home technology can be used to control water, heat and energy in the entire house. This paper investigates how smart home technology could be effectively utilised to aid the UK government in meeting climate change targets and to mitigate the environmental impact of a home in use towards reducing carbon emissions. Design/methodology/approach Both primary and secondary data were sought to gain insight into the research problem. An epistemological approach to this research is to use interpretivism to analyse data gathered via a semi-structured survey. Two groups of participants were approached: (1) professionals who are deemed knowledgeable about smart home development and implementation and (2) users of smart home technology. A variety of open-ended questions were formulated, allowing participants to elaborate by exploring issues and providing detailed qualitative responses based on their experience in this area which were interpreted quantitatively for clearer analysis. Findings With fossil fuel reserves depleting, there is an urgency for renewable, low carbon energy sources to reduce the 5 tonnes annual carbon emissions from a UK household. This requires a multi-faceted and a multimethod approach, relying on the involvement of both the general public and the government in order to be effective. By advancing energy grids to make them more efficient and reliable, concomitant necessitates a drastic change in the way of life and philosophy of homeowners when contemplating a reduction of carbon emissions. If both parties are able to do so, the UK is more likely to reach its 2050 net-zero carbon goal. The presence of a smart meter within the household is equally pivotal. It has a positive effect of reducing the amount of carbon emissions and hence more need to be installed. Research limitations/implications Further research is needed using a larger study sample to achieve more accurate and acceptable generalisations about any future course of action. Further investigation on the specifics of smart technology within the UK household is also needed to reduce the energy consumption in order to meet net-zero carbon 2050 targets due to failures of legislation. Practical implications For smart homes manufacturers and suppliers, more emphasis should be placed to enhance compatibility and interoperability of appliances and devices using different platform and creating more user's friendly manuals supported by step-by-step visual to support homeowners in the light of the wealth of knowledge base generated over the past few years. For homeowners, more emphasis should be placed on creating online knowledge management platform easily accessible which provide virtual support and technical advice to home owners to deal with any operational and technical issues or IT glitches. Developing technical design online platform for built environment professionals on incorporating smart sensors and environmentally beneficial technology during early design and construction stages towards achieving low to zero carbon homes. Originality/value This paper bridges a significant gap in the body of knowledge in term of its scope, theoretical validity and practical applicability, highlighting the impact of using smart home technology on the environment. It provides an insight into how the UK government could utilise smart home technology in order to reduce its carbon emission by identifying the potential link between using smart home technology and environmental sustainability in tackling and mitigating climate change. The findings can be applied to other building types and has the potential to employ aspects of smart home technology in order to manage energy and water usage including but not limited to healthcare, commercial and industrial buildings.
... However, at the same time as reducing the electricity bill and PAR, the consumer's comfort can be negotiated. The binary particle swarm optimization (BPSO) dependent EMC described in Ullaha et al. (2015) for a home energy management system (HEMS) with TOU cost and RESs has been adopted for scheduling the instruments for increasing the saving on the electricity bill. The saving on the electricity bill has been incremented at the cost of the consumer's comforts. ...
DESCRIPTION In a smart grid, the integration of renewable energy sources (RESs) can be done by users who have the capacity to participate in demand-side management. This chapter presents a home energy management system that includes a management controller for efficient scheduling of household loads and integration of RESs. An energy management control system that depends on the genetic wind-driven optimization technique for scheduling the individual instruments and combined houses is presented in this chapter. Further, the proposed model has been compared with genetic algorithm, binary particle swarm optimization, wind-driven optimization, and other already available models. Real-time pricing and time of use are grouped to price the energy. Efficiently integrating the RESs into a smart grid is a big challenge because of the time-varying and infrequent nature of RESs. The proposed control gives quick responses under constant and variable scenarios. The results have also been compared with already present techniques. The developed system is demonstrated by using MATLAB/Simulink.
... show that established study achieve promising results with high level of robustness against other methodologies. Also, Ihsan Ullah et al. [125] developed a novel appliance scheduling model based on the combination of GA and BPSO algorithms. The model is designed to reduce the total cost of consumed electricity of different household appliances and PAR value under dynamic pricing environment. ...
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Due to the rapidly developing of electric power system across the world in response to technical, economic and environmental developments, modern power systems often operate proximate to their maximal limits, engendering voltage instability risks in electric grid. On the other hand, excessive penetration of renewable energy sources into electrical grids may lead to many problems and operational limit violations, such as over and under voltages, active power losses and overloading of transmission lines, power plants failure, voltage instability risks and users discomfort. These problems happen when the system exceeds maximal operational capability (MOC) limit. In this thesis, firstly, various meta-heuristic optimization techniques have been developed and implemented to deal with different power system problems, such as single and multi-objective optimal reactive power dispatch problem. Since the characteristics of optimal reactive power dispatch (ORPD) in nature non-linear and non-convex and are consisting of mix of discrete and continuous variables; some non-conventional optimization techniques are developed and adopted to deal with discrete ORPD problem in large-scale electric grids. Technology advancement for green energy and its integration to the electric power system (EPS) has gathered substantial interest in the last couple of decades. Incorporating such resources has proven to reduce power losses and improve the reliability of electrical network. However, hyper-production or hypo-production of these resources in electric grid has imposed additional operational and control issues in voltage-regulation, system stability, and feasibility of solutions. Renewables incorporation into electric grid has led to significant changes in the types of consumption as well as dramatic and direct changes in the needs of optimal planning and operation of electric grids. To assess the suitability of the nonclassical optimization techniques for modern power systems, stochastic optimal power flow (OPF) problem with uncertain reserves from renewable energy sources was studied under different scenarios, including valve pointe effect and gas emission. Simulation results demonstrated that with hybrid generation system we could benefit with 2.4 % per hour cost reduction compared to traditional grid that based only on thermal generations as power sources. In addition, a new application of slim mould algorithm for practical optimal power flow with integration of renewables was conducted on Algerian electricity grid (DZA114-220/60 kV). Different cases were studied, where the feasibility of solutions and all control variables are deeply discussed. Hence, hybrid generation system is more effective and viable than classical system. In smart grid, efficient load management can help balance, reduce the burdensome on the electric network, and minimize operational electricity-cost. Robust optimization is a method that is used increasingly in the scheduling of household loads through demand side control. To this end, demand side management (DSM) scheme based on Meta-heuristic optimization techniques is proposed for home energy management system. The main objective behind this study is to adjust the peak demand and offering the total energy required at minimum cost with high quality. More precisely, in order to make consumers aware of their effective and essential contributions to helping the operator system during emergency cases (requests during peak hours). Simulation results showed that the proposed DSM scheme based meta-heuristic algorithms enrolling higher reduction in the total energy cost up to 26% compared to the base case and peak average ratio is curtailed by 55 %. In overall, the obtained results demonstrate significant improvement in energy quality, electric power system security, and notable reduction of peak demand in offering total energy required at minimum cost.
The evolution towards Smart Grids (SGs) represents an important opportunity for modernization of the energy industry. It is characterized by a bidirectional flow of information and energy between consumers and suppliers. However, the rapid increase of energy demands in residential areas is becoming a challenging problem. In order to address this issue, Demand-Side Management (DSM) has proven to be an effective solution. In this paper, we propose LOSISH, a price-based Demand Response (DR) system for load scheduling in residential Smart Homes (SHs) that achieves a trade-off between electricity payments and consumer’s discomfort. Our proposed system considers Renewable Energy Sources (RESs), Battery Energy Storage System (BESS) and Plug-in Electric Vehicle (PEV). We formulate our scheduling as a constrained optimization problem and we propose a new hybrid algorithm to solve it. The latter combines two well known heuristic algorithms: Particle Swarm Optimization (PSO) and Binary Particle Swarm Optimization (BPSO). Moreover, we propose a new clustering algorithm based on Machine Learning (ML) to extract consumer’s preferences from a real dataset that contains the historical consumption patterns of his smart appliances. We test our approach on real data traces obtained from a SH and we set up an experiment to evaluate our algorithm on a Raspberry Pi and measure its energy consumption. To prove the effectiveness of our approach, we compare our results with another approach from the literature in terms of electricity bill, Peak-to-Average Ratio (PAR), energy consumption, and execution time. Numerical results show that LOSISH outperforms the other approach in terms of electricity bill (up to 52.92% cheaper), PAR (up to 44% decrease in peak demands), energy consumption (up to 69.44% less consumption), and execution time (up to 63.15% faster).
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Power distribution networks at the distribution level are becoming more complex in their behavior and more heavily stressed due to the growth of decentralized energy sources. Demand response (DR) programs can increase the level of flexibility on the demand side by discriminating the consumption patterns of end-users from their typical profiles in response to market signals. The exploitation of artificial intelligence (AI) methods in demand response applications has attracted increasing interest in recent years. Particle swarm optimization (PSO) is a computational intelligence (CI) method that belongs to the field of AI and is widely used for resource scheduling, mainly due to its relatively low complexity and computational requirements and its ability to identify near-optimal solutions in a reasonable timeframe. The aim of this work is to evaluate different PSO methods in the scheduling and control of different residential energy resources, such as smart appliances, electric vehicles (EVs), heating/cooling devices, and energy storage. This review contributes to a more holistic understanding of residential demand-side management when considering various methods, models, and applications. This work also aims to identify future research areas and possible solutions so that PSO can be widely deployed for scheduling and control of distributed energy resources in real-life DR applications.
Demand-side management (DSM) is performing an important role in the future of smart grid, by managing and monitoring the loads in a smart and optimal way. In the literature, several works contribute to the development of optimization methods to deal with DSM in residential sectors. Three common challenges are presented: (1) reducing the electricity payment, (2) reducing the peak-to-average ratio, and (3) maximizing the users’ comfort. A great number of publications carry out with the energy management in a smart house, which aimed at minimizing the electricity cost and maximizing the users’ comfort. However, those that integrate renewable energy sources (RESs) have not received much attention. To deal with this issue, this study presents a multiobjective optimization technique for residential DSM over a day. The main contributions of the present work are: first, a novel approach to control residential load, taking into consideration that maximization of the users’ comfort and minimization of the electricity cost, is established. Second, RESs and battery storage systems are introduced to reduce further the electricity payment and the discomfort. Third, a mixed-integer linear programming algorithm is used to optimally allocate the electrical power according to the objective function. Constraints, including daily energy requirements, user preferences and energy availability from solar panels and batteries are considered. Three scenarios are proposed: the first one adopts the main grid as the only existing source of energy. The second one combines the main grid and solar panels. And the last one is a combination of the main grid, solar panels, and energy storage system. The results obtained for the different scenarios reveal that the proposed method offers a maximum satisfaction of the user with a minimum cost and demonstrate an optimal use of solar panels and battery storage. Finally, the daily cost and waiting time are compared for the previously proposed scenarios.
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Continuous advancements in Information and Communication Technology and the emergence of the Big Data era have altered how traditional power systems function. Such developments have led to increased reliability and efficiency, in turn contributing to operational, economic, and environmental improvements and leading to the development of a new technique known as Demand Side Management or DSM. In essence, DSM is a management activity that encourages users to optimize their electricity consumption by controlling the operation of their electrical appliances to reduce utility bills and their use during peak times. While users may save money on electricity costs by rescheduling their power consumption, they may also experience inconvenience due to the inflexibility of getting power on demand. Hence, several challenges must be considered to achieve a successful DSM. In this work, we analyze the power scheduling techniques in Smart Houses as proposed in most cited papers. We then examine the advantages and drawbacks of such methods and compare their contributions based on operational, economic, and environmental aspects.
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The aim of this paper is to develop a new Time of Use (TOU) tariff scheme for Demand Side Management (DSM) of residential consumers in Bangladesh. In this advanced era of technology, the requirement of electrical energy is continuously increasing. However, the overall growth of electricity generation is comparatively slower due to depletion of fuel sources. In this situation, a strategic plan needs to be implemented at consumer end to mitigate the energy crisis. As such, the concept of DSM is introduced where consumers can save electricity bills by changing their consumption pattern. It subsequently reduces the generation requirement during peak hours, which offers operational flexibility and financial benefits to utility. In the literature, DSM has been used in residential, commercial, and industrial sectors. However, the existing techniques do not explicitly consider the low-income consumers when TOU scheme is utilised. It may result in financial repercussion for these consumers. To overcome this challenge, a TOU scheme is proposed in this paper by ensuring financial benefits for all types of residential consumers and utility. To this end, an optimisation model is formulated by taking into account the various consumer groups such as low-income, middle-income and high-income. Two different meters (meter 1 and meter 2) are prudently allocated to these consumers. Using genetic algorithm technique, optimisation model is solved to provide electricity tariff and block sizes for various consumers under two meters. The proposed algorithm is applied to the residential inhabitants of a practical distribution system in Bangladesh. The results suggest that significant amount of annual savings can be achieved simultaneously by consumers and utility. The low-income consumers remain financially least affected while the proposed TOU tariff scheme is executed. In addition, the proposed TOU approach is compared to existing methods and found that optimisation based TOU outperforms the conventional ones.
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Distributed generation (DG) uses many small on-site energy harvesting deployments at individual buildings to generate electricity. DG has the potential to make generation more efficient by reducing transmission and distribution losses, carbon emissions, and demand peaks. However, since renewables are intermittent and uncontrollable, buildings must still rely, in part, on the electric grid for power. While DG deployments today use net metering to offset costs and balance local supply and demand, scaling net metering for intermittent renewables to a large fraction of buildings is challenging. In this paper, we explore an alternative approach that combines market-based electricity pricing models with on-site renewables and modest energy storage (in the form of batteries) to incentivize DG. We propose a system architecture and optimization algorithm, called GreenCharge, to efficiently manage the renewable energy and storage to reduce a building's electric bill. To determine when to charge and discharge the battery each day, the algorithm leverages prediction models for forecasting both future energy demand and future energy harvesting. We evaluate GreenCharge in simulation using a collection of real-world data sets, and compare with an oracle that has perfect knowledge of future energy demand/harvesting and a system that only leverages a battery to lower costs (without any renewables). We show that GreenCharge's savings for a typical home today are near 20%, which are greater than the savings from using only net metering.
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A home energy management (HEM) system is an integral part of a smart grid that can potentially enable demand response applications for residential customers. This paper presents an intelligent HEM algorithm for managing high power consumption household appliances with simulation for demand response (DR) analysis. The proposed algorithm manages household loads according to their preset priority and guarantees the total household power consumption below certain levels. A simulation tool is developed to showcase the applicability of the proposed algorithm in performing DR at an appliance level. This paper demonstrates that the tool can be used to analyze DR potentials for residential customers. Given the lack of understanding about DR potentials in this market, this work serves as an essential stepping-stone toward providing an insight into how much DR can be performed for residential customers.
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This paper proposes a new strategy to meet the controllable heating, ventilation, and air conditioning (HVAC) load with a hybrid-renewable generation and energy storage system. Historical hourly wind speed, solar irradiance, and load data are used to stochastically model the wind generation, photovoltaic generation, and load. Using fuzzy C-Means (FCM) clustering, these data are grouped into 10 clusters of days with similar data points to account for seasonal variations. In order to minimize cost and increase efficiency, we use a GA-based optimization approach together with a two-point estimate method. Minimizing the cost function guarantees minimum PV and wind generation installation as well as storage capacity selection to supply the HVAC load. Different scenarios are examined to evaluate the efficiency of the system with different percentages of load shifting. The maximum capacity of the storage system and excess energy are calculated as the most important indices for energy efficiency assessment. The cumulative distribution functions of these indices are plotted and compared. A smart-grid strategy is developed for matching renewable energy generation (solar and wind) with the HVAC load.
Demand Response (DR) and Time-of-Use (TOU) pricing refer to programs which offer incentives to customers who curtail their energy use during times of peak demand. In this paper, we propose an integrated solution to predict and re-engineer the electricity demand (e.g., peak load reduction and shift) in a locality at a given day/time. The system presented in this paper expands DR to residential loads by dynamically scheduling and controlling appliances in each dwelling unit. A decision-support system is developed to forecast electricity demand in the home and enable the user to save energy by recommending optimal run time schedules for appliances, given user constraints and TOU pricing from the utility company. The schedule is communicated to the smart appliances over a self-organizing home energy network and executed by the appliance control interfaces developed in this study. A predictor is developed to predict, based on the user's life style and other social/environmental factors, the potential schedules for appliance run times. An aggregator is used to accumulate predicted demand from residential customers.
With the development of smart grid, residents have the opportunity to schedule their power usage in the home by themselves for the purpose of reducing electricity expense and alleviating the power peak-to-average ratio (PAR). In this paper, we first introduce a general architecture of energy management system (EMS) in a home area network (HAN) based on the smart grid and then propose an efficient scheduling method for home power usage. The home gateway (HG) receives the demand response (DR) information indicating the real-time electricity price that is transferred to an energy management controller (EMC). With the DR, the EMC achieves an optimal power scheduling scheme that can be delivered to each electric appliance by the HG. Accordingly, all appliances in the home operate automatically in the most cost-effective way. When only the real-time pricing (RTP) model is adopted, there is the possibility that most appliances would operate during the time with the lowest electricity price, and this may damage the entire electricity system due to the high PAR. In our research, we combine RTP with the inclining block rate (IBR) model. By adopting this combined pricing model, our proposed power scheduling method would effectively reduce both the electricity cost and PAR, thereby, strengthening the stability of the entire electricity system. Because these kinds of optimization problems are usually nonlinear, we use a genetic algorithm to solve this problem.
Demand response is a key feature of the smart grid. The addition of bidirectional communication to today's power grid can provide real-time pricing (RTP) to customers via smart meters. A growing number of appliance companies have started to design and produce smart appliances which embed intelligent control modules to implement residential demand response based on RTP. However, most of the current residential load scheduling schemes are centralized and based on either day-ahead pricing (DAP) or predicted price, which can deviate significantly from the RTP. In this paper, we propose an opportunistic scheduling scheme based on the optimal stopping rule as a real-time distributed scheduling algorithm for smart appliances' automation control. It determines the best time for appliances' operation to balance electricity bill reduction and inconvenience resulting from the operation delay. It is shown that our scheme is a distributed threshold policy when no constraint is considered. When a total power constraint exists, the proposed scheduling algorithm can be implemented in either a centralized or distributed fashion. Our scheme has low complexity and can be easily implemented. Simulation results validate proposed scheduling scheme shifts the operation to off-peak times and consequently leads to significant electricity bill saving with reasonable waiting time.
This paper proposes an energy scheduling model and optimization algorithms for residential electricity consumers who attempt to optimally schedule their electricity consumption, generation and storage in a dynamic pricing environment. We describe an optimization problem which integrates electric, thermodynamic, economic, comfort, and possibly environmental parameters. We present the algorithmic solution and provide simulations results, based on a robust optimization approach which minimizes the impact of stochastic input on the objective function. We argue that the scheduling problem is complex enough to be beyond the analytical capabilities of average residential customers. This result supports the need of scheduling controllers deployed as Home Energy Management Systems (HEMS).
The drastic reduction of carbon emission to combat global climate change cannot be realized without a significant contribution from the electricity sector. Renewable energy resources must take a bigger share in the generation mix, effective demand response must be widely implemented, and high-capacity energy storage systems must be developed. A smart grid is necessary to manage and control the increasingly complex future grid. Certain smart grid elements-renewables, storage, microgrid, consumer choice, and smart appliances-increase uncertainty in both supply and demand of electric power. Other smart gird elements-sensors, smart meters, demand response, and communications-provide more accurate information about the power system and more refined means of control. Simply building hardware for renewable generators and the smart grid, but still using the same operating paradigm of the grid, will not realize the full potential for overall system efficiency and carbon reduction. In this paper, a new operating paradigm, called risk-limiting dispatch, is proposed. It treats generation as a heterogeneous commodity of intermittent or stochastic power and uses information and control to design hedging techniques to manage the risk of uncertainty.
Demand response (DR) is becoming an integral part of power system and market operations. Smart grid technologies will further increase the use of DR in everyday operations. Once the volume of the DR reaches a certain threshold, the effect of the DR events on the distribution and transmission system operations will be hard to ignore. This paper proposes changing the business process of DR scheduling and implementation by integrating DR with distribution grid topology. Study cases using OATI webDistribute show the potential DR effect on distribution grid operations and the distribution grid changing the effectiveness of the DR. These examples illustrate the need of integrating demand response with the distribution grid.
Demand response (DR), distributed generation (DG), and distributed energy storage (DES) are important ingredients of the emerging smart grid paradigm. For ease of reference we refer to these resources collectively as distributed energy resources (DER). Although much of the DER emerging under smart grid are targeted at the distribution level, DER, and more specifically DR resources, are considered important elements for reliable and economic operation of the transmission system and the wholesale markets. In fact, viewed from transmission and wholesale operations, sometimes the term ??virtual power plant?? is used to refer to these resources. In the context of energy and ancillary service markets facilitated by the independent system operators (ISOs)/regional transmission organizations (RTOs), the market products DER/DR can offer may include energy, ancillary services, and/or capacity, depending on the ISO/RTO market design and applicable operational standards. In this paper we first explore the main industry drivers of smart grid and the different facets of DER under the smart grid paradigm. We then concentrate on DR and summarize the existing and evolving programs at different ISOs/RTOs and the product markets they can participate in. We conclude by addressing some of the challenges and potential solutions for implementation of DR under smart grid and market paradigms.