![Electricity generation and cut-in, cut-out wind speed [55]](https://www.researchgate.net/profile/Sheraz-Aslam/publication/327932706/figure/fig1/AS:675788965818373@1538132053333/Electricity-generation-and-cut-in-cut-out-wind-speed-55_Q320.jpg)
Content uploaded by Sheraz Aslam
Author content
All content in this area was uploaded by Sheraz Aslam on Sep 28, 2018
Content may be subject to copyright.
A preview of the PDF is not available
An optimal home energy management scheme considering grid connected microgrids with day-ahead weather forecasting using artificial neural network
Abstract and Figures
Demand response (DR) strategy provides an opportunity to electricity consumers
to participate in making power system reliable by managing their electricity consumption. Due to increasing population, a lot of energy is consumed in the residential sector. Therefore, in this thesis, we propose an optimal scheme to systematically manage the energy consumption in residential area. Electricity cost and
peak to average ratio reduction are the main goals of this study. Furthermore,
reduction in imported electricity from the external grid is also the objective of
this study. The proposed scheme schedules smart appliances and electrical vehicle (EV) charging/discharging optimally according to the consumers’ preferences.
Each consumer has its own grid-connected microgrid for electricity generation;
which consists of wind turbine, solar panel, micro gas turbine and energy storage
system (ESS). On the other hand, a real time forecasting of wind speed and temperature has been performed using an artificial neural network for efficient energy
management. Furthermore, the scheduling problem is mathematically formulated
and solved by mixed integer linear programming (MILP). We also provide the
comparison of the optimal solutions, while considering EV with and without discharging capabilities. Findings from simulations affirm our proposed scheme in
terms of above-mentioned objectives.
After solving home energy management and EV charging/discharging problem using MILP, power trading problem is considered in this thesis to earn maximum
profit and proper utilization of renewable energy sources (RES). This problem is
solved using two well-known heuristic approaches: the cuckoo search algorithm
(CSA) and strawberry algorithm (SA). In our proposed scheme, a smart home
decides to buy or sell electricity from/to the commercial grid for minimizing electricity costs and PAR with earning maximization. It makes a decision on the basis
of electricity prices, demand and generation from its own microgrid. In this case,
the microgrid also consists of a wind turbine and solar panel. Electricity generation from the solar panel and wind turbine is intermittent in nature. Therefore,
ESS is also considered for stable and reliable power system operation. We test our
proposed scheme on a set of different case studies. The simulation results affirm
our proposed scheme in terms of electricity cost and PAR reduction with profit
maximization. Furthermore, a comparative analysis is also performed to show the
ixlegitimacy and productiveness of CSA and SA
Figures - uploaded by Sheraz Aslam
Author content
All figure content in this area was uploaded by Sheraz Aslam
Content may be subject to copyright.
... However, hourly price scheduling is rather challenging because the price fluctuates between on-peak and off-peak hours. Load forecasting can be employed to address this issue [59]. However, shiftable appliances are adjusted from on-peak to off-peak prices. ...
These days, users consume more electricity during peak hours, and electricity prices are typically higher between 3:00 p.m. and 11:00 p.m. If electric vehicle (EV) charging occurs during the same hours, the impact on residential distribution networks increases. Thus, home energy management systems (HEMS) have been introduced to manage the energy demand among households and EVs in residential distribution networks, such as a smart micro-grid (MG). Moreover, HEMS can efficiently manage renewable energy sources, such as solar photovoltaic (PV) panels, wind turbines, and vehicle energy storage. Until now, no HEMS has intelligently coordinated the uncertainty of smart MG elements. This paper investigated the impact of PV solar power, MG storage, and EVs on the maximum solar radiation hours. Several deep learning (DL) algorithms were utilized to account for the uncertainties. A reinforcement learning home centralized photovoltaic (RL-HCPV) scheduling algorithm was developed to manage the energy demand between the smart MG elements. The RL-HCPV system was modelled according to several constraints to meet household electricity demands in sunny and cloudy weather. Additionally, simulations demonstrated how the proposed RL-HCPV system could incorporate uncertainty, and efficiently handle the demand response and how vehicle-to-home (V2H) can help to level the appliance load profile and reduce power consumption costs with sustainable power production. The results demonstrated the advantages of utilizing RL and V2H technology as potential smart building storage technology.
... In this context, governments and the scientific community face major difficulties related to lowering electricity costs and ensuring environmental sustainability. Due to the long-term effects of carbon emissions from traditional power plants, some countries have imposed large taxes on carbon emissions [2][3][4]. Therefore, the research community has made tremendous research efforts to reduce electricity costs and carbon emissions [5][6][7][8]. ...
Fossil-fuel-based power generation leads to higher energy costs and environmental impacts. Solar and wind energy are abundant important renewable energy sources (RES) that make the largest contribution to replacing fossil-fuel-based energy consumption. However, the uncertain solar radiation and highly fluctuating weather parameters of solar and wind energy require an accurate and reliable forecasting mechanism for effective and efficient load management, cost reduction, green environment, and grid stability. From the existing literature, artificial neural networks (ANN) are a better means for prediction, but the ANN-based renewable energy forecasting techniques lose prediction accuracy due to the high uncertainty of input data and random determination of initial weights among different layers of ANN. Therefore, the objective of this study is to develop a harmony search algorithm (HSA)-optimized ANN model for reliable and accurate prediction of solar and wind energy. In this study, we combined ANN with HSA and provided ANN feedback for its weights adjustment to HSA, instead of ANN. Then, the HSA optimized weights were assigned to the edges of ANN instead of random weights, and this completes the training of ANN. Extensive simulations were carried out and our proposed HSA-optimized ANN model for solar irradiation forecast achieved the values of MSE = 0.04754, MAE = 0.18546, MAPE = 0.32430%, and RMSE = 0.21805, whereas our proposed HSA-optimized ANN model for wind speed prediction achieved the values of MSE = 0.30944, MAE = 0.47172, MAPE = 0.12896%, and RMSE = 0.55627. Simulation results prove the supremacy of our proposed HSA-optimized ANN models compared to state-of-the-art solar and wind energy forecasting techniques.
... Energy management by deep learning is the process in which energy generation and consumption is observed, planned and controlled. The cost of electric bills of consumers can be reduced by better energy management [121][122]. Energy management integrates the renewable energy source (RES) and energy storage system (ESS) in power systems [121]. ...
The main and pivot part of electric companies is the load forecasting. Decision-makers and think tank of power sectors should forecast the future need of electricity with large accuracy and small error to give uninterrupted and free of load shedding power to consumers. The demand of electricity can be forecasted amicably by many Machine Learning (ML), Deep Learning (DL) and Artificial Intelligence (AI) techniques among which hybrid methods are most popular. The present technologies of load forecasting and present work regarding combination of various ML, DL and AI algorithms are reviewed in this paper. The comprehensive review of single and hybrid forecasting models with functions; advantages and disadvantages are discussed in this paper. The comparison between the performance of the models in terms of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) values are compared and discussed with literature of different models to support the researchers to select the best model for load prediction. This comparison validates the fact that the hybrid forecasting models will provide a more optimal solution. INDEX TERMS load forecasting, machine learning, load shedding, root mean squared error, mean absolute percentage error.
... Design models of machine learning were also designed to automatically control the appliances based on user behaviour patterns. The scheduling problem is initially solved by mixed integer linear programming and later reduction in cost and Peak to Average Ratio (PAR) with maximization of profit is achieved by cuckoo search algorithm and strawberry algorithms [11]. A novel multi objective problem was formulated for a HEMS to maintain a trade-off between cost and comfort where Particle Swarm Optimization (PSO) was engaged to solve it [12]. ...
The implementation of Home Energy Management System (HEMS) in the recent research works have directed to revolutions in the conservation of energy and the environment along with savings in cost and enhancement in comfort levels. The implication of carrying out this research is to propose a home energy management approach combined with the insertion of Plug-in Electric Vehicles (PEVs) into the system to minimize the complete expense of electrical energy without losing the ease of the appliances. A Belgium house in a freezing climate and Time of Use (ToU) demand response program is probed in the following case study. The Water Filling Energy distribution algorithm is implemented in the study without violating the constraints of the overall system and individual appliances such as thermal comforts of Heat Pump (HP), Combined Heat and Power (CHP) units, State of Charge (SOC) limits of batteries and time restrictions of Washing Machine (WM), Cloth Dryer (CD) and Dish Washer (DW) The objectives are formulated as an optimization problem and are solved by Genetic Algorithm. On this account, the consequential effects on the grid energy deviation, SOC and lifetime of the battery are investigated. Thereby, the stress on the grid and battery are also explored.
... Higher energy consumption leads to higher carbon emissions that adversely affect the environment. For the sake of environmental sustainability, some countries have imposed a tax on carbon emissions [3], [9], [10]. Therefore, there is an exigent need to reduce the power consumption of DCs or to install cheaper resources of energy generation that also lead to lower carbon emissions. ...
This study investigates the energy cost and carbon emission reduction problem in geographically distributed cloud data centers (DCs), where each DC is connected with its own renewable energy resources (RERs) for green energy generation. We consider four cloud DCs that are operated by a single cloud service provider. They consume energy from both RERs and from the commercial grid to meet the demand of cloud users. For energy pricing, we consider four different energy markets that offer varying energy prices per hour. Additionally, our proposed strategy enables DCs to sell excess electricity to the commercial grid in peak-price hours and purchase in low-cost hours according to power trading. This work also exploits energy storage devices (ESDs) to store energy for future use. We utilize real-time data requests, weather data, and pricing data for performing simulations and results affirm the effectiveness and productiveness of our proposed method to mitigate the energy cost and carbon emission of cloud DCs.
... Such accurate forecasting enables the power utility operator to efficiently manage supply with demand and generate excess electricity from brown energy sources in case of less energy generation from renewable sources. The forecasting of solar energy generation is a complex task because it completely depends on weather conditions (e.g., temperature, humidity, solar irradiance, and cloud) [6], [7], [8]. Various forecasting models are used to predict solar energy in the literature and [16] these models are categorized into physical, statistical, and empirical models. ...
In the last few years, carbon emissions and energy demand have increased dramatically around the globe due to a surge in population and energy-consuming devices. The integration of renewable energy resources (RERs) in a power supply system provides an efficient solution in terms of low energy cost with lower carbon emissions. However, renewable sources like solar panels have irregular nature of power generation because of their dependence on weather conditions, such as solar radiation, humidity, and temperature. Therefore, to tackle this intermittent nature of solar energy, power prediction is necessary for efficient energy management. Deep learning and machine learning-based methods have frequently been implemented for energy forecasting in the literature. The current work summarizes the state-of-the-art deep learning-based methods that are proposed to forecast the solar power for proper energy management. We also explain the methodologies of solar energy forecasting along with their outcomes. At the end, future challenges and opportunities are uncovered in the application of deep and machine learning in this area.
This study proposes an efficient energy management method to systematically manage the energy consumption in the residential area to alleviate the peak to average ratio and mitigate electricity cost along with user comfort maximiza-tion. We developed an efficient energy management scheme using mixed integer linear programming (MILP), which schedules smart appliances and charg-ing/discharging of electric vehicles (EVs) optimally in order to mitigate energy costs. In the proposed model, consumer is able to generate its own energy from microgrid consisting of solar panels and wind turbines. We also consider an energy storage system (ESS) for efficient energy utilization. This work also performs energy forecasting using wind speed and solar radiation prediction for efficient energy management. Moreover, we perform extensive simulations to validate our developed MILP based scheme and results affirm the effectiveness and productiveness of our proposed energy efficient technique.
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.
Demand side management (DSM) in smart grid authorizes consumers to make informed decisions regarding their energy consumption pattern and helps the utility in reducing the peak load demand during an energy stress time. This results in reduced carbon emission, consumer electricity cost, and increased grid sustainability. Most of the existing DSM techniques ignore priority defined by consumers. In this paper, we present priority-induced DSM strategy based on the load shifting tech-nique considering various energy cycles of an appliance. The day-ahead load shifting technique proposed is mathematically formulated and mapped to multiple knapsack problem to mitigate the rebound peaks. The autonomous energy management controller proposed embeds three meta-heuristic optimization techniques; genetic algorithm, enhanced differential evolu-tion, and binary particle swarm optimization along with optimal stopping rule, which is used for solving the load shifting problem. Simulations are carried out using three different appliances and the results validate that the proposed DSM strategy successfully shifts the appliance operations to off-peak time slots, which consequently leads to substantial electricity cost savings in reasonable waiting time, and also helps in reducing the peak load demand from the smart grid. In addition, we calculate the feasible regions to show the relationship between cost, energy consumption, and delay.
A priority-induced demand side management system to mitigate rebound peaks using multiple knapsack. Available from: https://www.researchgate.net/publication/323945280_A_priority-induced_demand_side_management_system_to_mitigate_rebound_peaks_using_multiple_knapsack [accessed Mar 26 2018].
With the rapid advancement in technology, electrical energy consumption is increasing rapidly. Especially, in the residential sector, more than 80% of electrical energy is being consumed because of consumer negligence. This brings the challenging task of maintaining the balance between the demand and supply of electric power. In this paper, we focus on the problem of load balancing via load scheduling under utility and rooftop photovoltaic (PV) units to reduce electricity cost and peak to average ratio (PAR) in demand-side management. For this purpose, we adopted genetic algorithm (GA), binary particle swarm optimization (BPSO), wind-driven optimization (WDO), and our proposed genetic WDO (GWDO) algorithm, which is a hybrid of GA and WDO, to schedule the household load. For energy cost estimation, combined real-time pricing (RTP) and inclined block rate (IBR) were used. The proposed algorithm shifts load from peak consumption hours to off-peak hours based on combined pricing scheme and generation from rooftop PV units. Simulation results validate our proposed GWDO algorithm in terms of electricity cost and PAR reduction while considering all three scenarios which we have considered in this work: (1) load scheduling without renewable energy sources (RESs) and energy storage system (ESS), (2) load scheduling with RESs, and (3) load scheduling with RESs and ESS. Furthermore, our proposed scheme reduced electricity cost and PAR by 22.5% and 29.1% in scenario 1, 47.7% and 30% in scenario 2, and 49.2% and 35.4% in scenario 3, respectively, as compared to unscheduled electricity consumption.
Over the past few years, active research on algorithm development for the optimal operations of home energy management systems (HEMSs) has been performed. The objective is to compute optimized schedules for shiftable home appliances. This is based on the demand response (DR) synergized with renewable energy sources and energy storage system optimal dispatch (DRSREOD). An improved algorithm for a DRSREOD-based HEMS is proposed in this paper. This heuristic-based algorithm considers DR, photovoltaic availability, the state of charge and charge/discharge rates of the storage battery and the sharing-based parallel operation of more than one power source to supply the required load. The HEMS problem has been solved to minimize the cost of energy (
$CE$
) and time-based discomfort (
$TBD$
) 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
$TBD$
performance parameters. An inclining block rate scheme is also incorporated to reduce the peak load. A set of optimized tradeoffs between
$CE$
and
$TBD$
has been computed to address multi-objectivity using a multi-objective genetic algorithm (MOGA) with Pareto optimization (PO) to perform the tradeoff analysis and to enable consumers to select the most feasible solution. Due to the rapid increase in demand for electricity, developing countries are facing large-scale load shedding (LS). An innovative algorithm is also proposed for the optimal sizing of a dispatchable generator (DG) that can supply the DRSREOD-based HEMS during LS hours to ensure an uninterrupted supply of power. The proposed MOGA/PO-based algorithm enables consumers to select a DG of the optimal size from among a number of optimal choices based on tradeoffs between the DG size,
$CE$
, and
$TBD$
.
The smart grid plays a vital role in decreasing electricity cost through Demand Side
Management (DSM). Smart homes, a part of the smart grid, contribute greatly to minimizing electricity
consumption cost via scheduling home appliances. However, user waiting time increases due to
the scheduling of home appliances. This scheduling problem is the motivation to find an optimal
solution that could minimize the electricity cost and Peak to Average Ratio (PAR) with minimum user
waiting time. There are many studies on Home Energy Management (HEM) for cost minimization
and peak load reduction. However, none of the systems gave sufficient attention to tackle multiple
parameters (i.e., electricity cost and peak load reduction) at the same time as user waiting time
was minimum for residential consumers with multiple homes. Hence, in this work, we propose an
efficient HEM scheme using the well-known meta-heuristic Genetic Algorithm (GA), the recently
developed Cuckoo Search Optimization Algorithm (CSOA) and the Crow Search Algorithm (CSA),
which can be used for electricity cost and peak load alleviation with minimum user waiting time.
The integration of a smart Electricity Storage System (ESS) is also taken into account for more
efficient operation of the Home Energy Management System (HEMS). Furthermore, we took the
real-time electricity consumption pattern for every residence, i.e., every home has its own living
pattern. The proposed scheme is implemented in a smart building; comprised of thirty smart homes
(apartments), Real-Time Pricing (RTP) and Critical Peak Pricing (CPP) signals are examined in
terms of electricity cost estimation for both a single smart home and a smart building. In addition,
feasible regions are presented for single and multiple smart homes, which show the relationship
among the electricity cost, electricity consumption and user waiting time. Experimental results
demonstrate the effectiveness of our proposed scheme for single and multiple smart homes in terms
of electricity cost and PAR minimization. Moreover, there exists a tradeoff between electricity cost
and user waiting.
This paper investigates the energy cost minimization problem for smart grids with distributed renewable energy resources. Unlike earlier research studies that either have assumed all the appliance jobs are interruptible or power-shiftable and that the electricity prices as well as the availability of renewable resources are known, this paper focuses on more challenging scenarios in which appliance jobs are non-interruptible and non-power-shiftable, the electricity prices vary with the overall load of the entire grid in real-time, and the renewable power generation is uncertain. Because home solar systems are widely available, this paper assumes that each consumer in the grid can have a photovoltaic system and a side battery. Collected solar energy can be used to meet a consumer's individual power demand, stored in the battery for future use, or sold back to the grid during peak hours to lower electricity bills and the overall load on the entire grid. To solve this problem, a two-stage robust optimization model is proposed, and the C&CG method is utilized to solve it. However, to solve the problem more efficiently when the number of consumers and appliance jobs is large, a second approach called SRDSM is proposed. The SRDSM algorithm consists of two parts: The first part is a job scheduling algorithm that minimizes electricity costs for all consumers. The second part is a power management algorithm based on dynamic programming that reduces the energy cost further by utilizing renewable energy. The numerical results show that, although the C&CG method produces optimal solutions, the SRDSM algorithm is much more scalable and efficient when the problem size is large.
In a smart grid, several optimization techniques have been developed to schedule load in the residential area. Most of these techniques aim at minimizing the energy consumption cost and the comfort of electricity consumer. Conversely, maintaining a balance between two conflicting objectives: energy consumption cost and user comfort is still a challenging task. Therefore, in this paper, we aim to minimize the electricity cost and user discomfort while taking into account the peak energy consumption. In this regard, we implement and analyse the performance of a traditional dynamic programming (DP) technique and two heuristic optimization techniques: genetic algorithm (GA) and binary particle swarm optimization (BPSO) for residential load management. Based on these techniques, we propose a hybrid scheme named GAPSO for residential load scheduling, so as to optimize the desired objective function. In order to alleviate the complexity of the problem, the multi dimensional knapsack is used to ensure that the load of electricity consumer will not escalate during peak hours. The proposed model is evaluated based on two pricing schemes: day-ahead and critical peak pricing for single and multiple days. Furthermore, feasible regions are calculated and analysed to develop a relationship between power consumption, electricity cost, and user discomfort. The simulation results are compared with GA, BPSO and DP, and validate that the proposed hybrid scheme reflects substantial savings in electricity bills with minimum user discomfort. Moreover, results also show a phenomenal reduction in peak power consumption.
In this work, we propose a DSM scheme for electricity expenses and peak to average ratio (PAR) reduction using two well-known heuristic approaches: the cuckoo search algorithm (CSA) and strawberry algorithm (SA). In our proposed scheme, a smart home decides to buy or sell electricity from/to the commercial grid for minimizing electricity costs and PAR with earning maximization. It makes a decision on the basis of electricity prices, demand and generation from its own microgrid. The microgrid consists of a wind turbine and solar panel. Electricity generation from the solar panel and wind turbine is intermittent in nature. Therefore, an energy storage system (ESS) is also considered for stable and reliable power system operation. We test our proposed scheme on a set of different case studies. The simulation results affirm our proposed scheme in terms of electricity cost and PAR reduction with profit maximization.
Demand response programs are developed to preserve the balance of demand-supply ratio and to reduce energy cost incurred by the electricity customer. Most of the existing work on residential demand response programs focused on utility-consumer interaction in a coordinating game model via two-way communication to achieve minimization of total cost and peak to average ratio. The prior work has considered the problem of energy consumption scheduling for multiple users but fails to address the optimally fair environment for scheduling. This paper proposes a consumer preference based demand response model formulated in a game-theoretic framework. The proposed model have a key feature that no consumer can acquire profit by deviating from the prescribed strategies or chronological order. The optimal game scheduling for minimizing the energy costs is achieved by using correlated equilibrium represented as Nash Equilibrium of energy consumption scheduling game. The simulation results clearly demonstrate the minimization of peak to average ratio up to 31.4% on an average for each day. The cost benefit of 261.6$ in a day is reaped by total users in the system which is highest among all days. The proof of existence of Nash Equilibrium is authenticated in the appendix.