Electricity Consumption Cost Research Articles

Comparative Analysis of the Performance of the Thermal Pasteurization System Using Ohmic Heating Method with an Electric Heater on Mango Puree

This study explains the comparative analysis of performance in the thermal pasteurization system of the ohmic heating method with the electric heater in mango puree. The goal is to find out which thermal pasteurization method is better and more efficient. The method used for this study is to compare the two thermal pasteurization methods from the performance obtained, heating time, electrical power and electricity consumption costs. In the pasteurization process, the product is heated not exceeding 70 oC in the ohmic tube with an electric current on an electrode with a thickness of 4 mm attached to the end of the ohmic tube, while pasteurization of the product electric heater is heated in a double jacket tube equipped with an electric heater. The results of the analysis were obtained for the ohmic heating method the efficiency value was 78%, while for the method with an electric heater the efficiency value was 4%, the heating time of the ohmic heating method is 555 seconds, while the heating time of the electric heater method is 1500 seconds, for electric power in the ohmic heating method is 224.59 watts, while in the electric heater method an electric power of 1730.2 watts is obtained, and the cost of electricity consumption of the ohmic heating method is Rp.1,498.33,-/month, while the electric heater method costs electricity consumption of Rp.31,407.26,-/month.

Optimizing Biomass Pre-Treatment Technologies for BBJP Plants in Indonesia: A Multi-Criteria Decision Making Approach

The challenges of energy consumption and environmental sustainability are pronounced in the dynamic landscape of contemporary industries driven by Industry 4.0 technologies. Indonesia, heavily reliant on fossil fuels, charts a course toward a clean energy future with a National Energy Transition Roadmap for Net Zero Emission by 2060. This transition involves innovative strategies such as biomass co-firing and waste utilization in Solid Recovered Fuel (SRF) plants, known as Bahan Bakar Jumputan Padat (BBJP) plants. To optimize these BBJP plants, this study employs Multi-Criteria Decision Making (MCDM) methodologies, specifically the Analytical Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), to evaluate and select pre-treatment technologies. Criteria include capacity, conversion process, waste type, electricity consumption, operational ease, land requirement, and investment cost. Comparing bio-drying, thermal drying, and mechanical drying, AHP ensures consistent criterion weights, with TOPSIS ranking bio-drying as the most favorable, followed by thermal and mechanical drying. The study acknowledges global waste management challenges and introduces a mobile-modular containerized BBJP/SRF plant model, addressing installation, maintenance, scalability, and adaptability issues. While recognizing challenges, especially in pre-treatment processes, the research emphasizes the need for efficient and cost-effective solutions. Practical implications include enhanced decision-making in biomass drying, identification of technology advantages and disadvantages, and a commitment to address challenges for sustainable implementation. The study contributes to Indonesia's energy transition discourse, advocating the pivotal role of BBJP plants in balancing Industry 4.0 demands and environmental protection, providing insights for stakeholders and decision-makers in advancing sustainable waste-to-energy initiatives.

Rural Photovoltaic Storage and Charging Integrated Charging Station Capacity Allocation Strategy based on Tariff Compensation Mechanism

Background: In order to help the carbon peaking and carbon neutrality goals, the current new energy vehicle to the countryside policy for the local use of renewable energy and demand-side carbon reduction provides a good opportunity but also requires rural townships and villages of electric vehicle charging infrastructure planning ahead. However, due to the current low rural electric vehicle ownership, the charging price compensation mechanism is not yet perfect, resulting in the planning period of electric vehicle growth and the willingness to respond to the tariff compensation policy is difficult to accurately assess. Methods: This paper proposes a rural photovoltaic storage and charging integrated charging station capacity allocation strategy based on the tariff compensation mechanism. Firstly, we construct a spatial-temporal dynamic distribution model of rural EV charging load coupled with distribution network-transportation network, and on this basis, we consider the rural EV charging time-sharing tariff and tariff compensation policy incentive, and amend the EV charging load transfer model; and then we construct an optimization planning model of charging station with the goal of minimizing the cost of construction, operation and maintenance, and maximizing the charging benefit of the integrated charging station in the rural area, and obtain the optimal synergistic planning scheme under the tariff compensation mechanism in the planning period. Results: The optimal collaborative planning scheme under the electricity price compensation mechanism is obtained, and the correctness and validity of the proposed optimal planning method of the rural optical storage charging station under the electricity price compensation mechanism is verified by the example, which is of positive significance in the promotion of the charging facilities to go to the countryside in an appropriate manner, and in the stimulation of the willingness of the rural consumers in the townships to purchase vehicles. Conclusion: (1) The spatial and temporal distribution and transfer model of rural EV charging loads with price incentives is constructed by taking into account the preferential charging price policies such as rural time-sharing tariff and tariff compensation mechanism. By comparing the operating revenues of optical storage-charging integrated charging stations with and without timesharing tariffs and tariff compensation policies, we verified the incentive effect of multiple types of price incentives for the over-planning of rural electric vehicle charging facilities. (2) The proposed optimal configuration method of rural photovoltaic, storage and charging integration charging station can realize the in-situ utilization of rural renewable energy, tap the price competitiveness of photovoltaic, storage and charging integration, and weaken the cost of electricity consumption. By comparing the optimized configuration scheme with and without joint planning, it is verified that the moderate configuration of in-situ photovoltaic and energy storage equipment on the basis of the planning of charging piles brings benefits far exceeding the investment cost, and has a great role in increasing the operational efficiency of rural charging facilities.

Influence of metal encapsulation on thermophysical properties and heat transfer in salt hydrate phase change material for air conditioning system

Phase change materials (PCM) are gaining increasing attention as a cold energy storage technology for air conditioning systems due to their potential to reduce electrical grid loading, energy consumption, and electricity costs. However, realizing the full potential of PCM-based cold energy storage in practical applications is constrained by low cold energy transfer efficiency within air conditioning systems. This study addresses this challenge by exploring the encapsulation of salt hydrate PCM using six different metals to enhance heat transfer efficiency. Additionally, the corrosion behaviors of the PCM towards these metals were thoroughly evaluated. Notably, copper demonstrated the highest corrosion rate, and the resulting corrosion products, particularly from copper and brass, were found to substantially reduce the energy storage capacity of PCM. Moreover, PCM encapsulated into aluminum alloy exhibited higher heat transfer efficiency than that of stainless steel. In light of these findings, aluminum alloy is recommended as the optimal choice for PCM encapsulation, given its corrosion resistance and minimal effect on thermal properties. This research provides crucial insights for selecting appropriate metals to enhance the efficiency and durability of air conditioning systems utilizing salt hydrate PCM.

A data center expansion scheme considering net-zero carbon operation: Optimization of geographical location, on-site renewable utilization and green certificate purchase

Recently, more and more global attention has been paid to data centers’ energy consumption and climate impact. Serving as the backbone of the digital world, the energy and carbon-intensive data centers have to find a green expansion way to fulfill their obligations in building a carbon-neutral society. Therefore, this paper proposes a net-zero carbon operation oriented data center expansion scheme for the central government to formulate incentive policies. The economic expansion model minimizes newly established data centers’ investment and electricity consumption costs. Geographical location, on-site renewable utilization, and green certificate purchase are integrated and considered to achieve net-zero carbon operation by reducing total electricity consumption, increasing green power consumption, and offsetting inevitable carbon emissions. A case study in China is conducted, and results under different geographical location scenarios, power supply mix scenarios, and green certificates market scenarios are analyzed and compared. It is found that the zero-carbon operation of new data centers mainly relies on clean main-grid power, but a more decentralized layout and a provincial green certificate market can increase the contribution of on-site renewable energy. Lastly, four detailed policy implications for stimulating China’s data center expansion are discussed.

Investment principles in value-target processes of electricity consumption at iron ore enterprises

Purpose. To establish the influence of investment on cost-target processes of electric consumption of iron ore enterprises by applying economic and mathematical models. Methodology. As a result of the research, system and critical analysis was applied in the synthesis of formation of economic and analytical indicators and processes of electricity consumption at iron ore enterprises. Methods of comparative analysis, functional-cost, economic-statistical research were used to determine cost-target characteristics when building economic-mathematical models. The general basis of the conducted study is a complex approach. Findings. The capitalization of relations in Ukraine requires a more careful organization of the practice of investing, in relation to the cost policy of electricity consumption at industrial enterprises in Ukraine. When mining iron ore, optimal matching of the cost of electricity consumption and investment investments, which make it possible to ensure the necessary volumes of production, is of great importance. Due to this, in modern conditions, the necessary digital support of investment processes is of great importance. An economic-mathematical model of the impact of investments on the cost-target problems of electric energy consumption is developed, which allows numerically determining the interrelationship of the selected indicators. The combination of theoretical study with practical implementation made it possible to investigate the conditions under which there is the best ratio of investment investments and the cost of electricity consumption. Originality. The method of influencing investment on the cost of electricity consumption by iron ore enterprises has received further development. An economic-mathematical model has been constructed that allows the proposed method to be implemented. Practical value. The analysis of the results of the cost of electricity consumption at iron ore enterprises allows us to estimate the statistical dependence of the cost of electricity consumption on the amount of investment. The conducted analysis made it possible to investigate the dependence of the values of the total average daily costs of consumed electricity, the cost of consumed electricity, the specific cost of electricity consumption on the amount of investment contributions at the enterprises of Kryvyi Rih Iron Ore Combine on the basis of regression models. It is expedient to determine the question of choosing an optimization option on the basis of an analysis of the real economic situation in accordance with the volume and cost of electricity consumption at iron ore enterprises. The practice of applying the proposed research results makes it possible to determine the most optimal option for energy efficiency management.

Routing a mixed fleet of electric and conventional vehicles under regulations of carbon emissions

This paper seeks to optimise fleet management for companies that utilise both internal combustion engine vehicles (ICEVs) and electric vehicles by considering various costs, including fixed costs, variable costs, time window penalty costs, fuel costs, and electricity consumption costs. Additionally, the study examines the influence of four types of regulations, i.e. carbon cap, carbon tax, carbon trading, and carbon offsetting, on carbon emissions and fleet configuration. The optimisation model was solved using a Clarke-Wright savings heuristic algorithm followed by an improved adaptive genetic algorithm (IAGA), and a sensitivity analysis was conducted under different regulatory policies. The results show that all four types of regulations can effectively reduce fleet emissions. While carbon prices have a greater impact on carbon regulations than carbon quota, carbon trading was more effective under similar circumstances. Therefore, governments should implement appropriate regulatory strategies to reduce energy consumption and encourage enterprises to reduce their emissions.

Research on Industrial Economic Development and Electricity Price Adjustment Strategy Based on Multi-Agent Game

With the deepening of the electricity market reform and the accelerating transformation of the new power system, the number of new electricity customers in the market with local development characteristics has increased. The local industrial and economic development have been affected by the ability to guide electricity prices. In this paper, using the multi-agent game based on exploring the impact of electricity price on the industrial economy, the specific optimization scheme of electricity price adjustment is proposed with the aim of improving the overall efficiency of the power grid and reducing the cost of electricity for industrial users. After the game, the overall efficiency of the electricity supplier will be increased by about 3.06 percent, and the cost of electricity used by various special industries will be reduced by about 6.86 percent to 4.48 percent. The win-win situation of the electricity suppliers’ income and the featured industries’ development can be achieved by the optimization results. As well as the cost of electricity consumption in featured industries can be reduced, and the comprehensive benefits of electricity suppliers can be improved by using the electricity price adjustment scheme after game interaction, which promotes the rational use of energy and meets the current social requirements for the green and sustainable development of the electricity industry.

Growth and Development of Leaf Vegetable Crops under Conditions of the Phytotechnical Complex in Antarctica

Ensuring the technical and technological possibility of regularly obtaining fresh, high-quality plant production in Antarctic stations is an urgent task of our time. This work is devoted to studying the growth and development of leaf vegetable crops and the main quality indicators of their edible parts when grown in the phytotechnical complex greenhouses at the “Vostok” Antarctic station and at the agrobiopolygon of the Agrophysical Research Institute (AFI). The plants, belonging to 13 varieties of 9 types of leaf vegetable crops (arugula, garden cress, cabbage, mustard, leaf radish, leaf lettuce, amaranth, dill, parsley leaf), were studied during five growing seasons at the “Vostok” station and at the AFI agrobiopolygon under controlled conditions (control). The experimental data obtained demonstrate the high productivity of the phytotechnical complex for most of the investigated crops per unit of useful area, with lower costs of electricity and water consumption per unit of production compared with a number of greenhouses at foreign Antarctic stations and greenhouse complexes with controlled conditions located on other continents. Lettuce crops were the most adapted to the growing conditions at the Antarctic station “Vostok”. They did not differ in their evaluated characteristics from the control. All other investigated crops, while not differing in their development rate and quality, had statistically significant (16–61%) decreases in their yield per 1 m2 per year. This may demonstrate the difference in the “genotype–environment” interaction in plants grown at the Antarctic station and AFI agrobiopolygon, probably due to the different barometric pressure and partial pressure of oxygen at the two locations. The positive psychological effects of the greenhouses were identified along with nutritional and other qualities of the plants.

Energetic performance evaluation of a corrugated channel solar air heater: Experimental investigation, mathematical modeling, and solution procedure

AbstractThe cost of manufacturing and electricity consumption are key considerations in encouraging the adoption of solar air heaters (SAH) in sunny and low‐income areas. These parameters can have a significant impact on promoting the use of these devices. In this study, solar air heating was designed with the objective of achieving the highest possible efficiency/cost ratio. It is a corrugated channel SAH whose structure is equipped with two barriers perforated with a sufficient number of holes for a good airflow distribution. A new model was developed to evaluate the qualitative parameters that describe the thermos‐energetic behavior of a heating system. These parameters were measured using experimental data obtained under real operating conditions. The thermal model assumes a uniform temperature for the glass, absorber, and insulation of the collector, while the temperature of the circulating air is assumed to vary linearly along the collector. To ensure that these assumptions were valid, the collector was cut into a number of 0.1 m sections in the direction of flow. By comparing the numerical results with the experimental data, the model was validated and then used to calculate the temperature profiles of the different elements of the collector, as well as to estimate the impact of certain operational parameters on its thermal performance. Relative percentage error values, between the numerical and the experimental results, of 1.7517%, 1.0750%, 0.8577%, 2.2371%, and 2.3637% for absorber plate temperature, outlet airflow temperature, useful power, thermal, and effective efficiencies, respectively, are recorded.

Integrated Energy Management of Smart Grids in Smart Cities Based on PSO Scheduling Models

The supporting position and role of smart grids in the construction of smart cities have not been fully explored. Based on systematizing the system architecture of smart cities, we first analyze the facilitating and constraining roles of smart grids and smart cities with each other and make a quantitative analysis of the coordination and supporting roles between them; in the smart grid environment, we propose a framework of energy management system based on particle swarm optimization (PSO) dispatching model. The algorithm optimizes the operation of dispatchable loads, electric vehicles, and energy storage systems based on outdoor temperature forecasts, renewable energy power output forecasts, day-ahead tariff signals, and customer preferences to minimize customer electricity costs. The performance of the algorithm is verified through simulation experiments, and the results show that the proposed algorithm significantly reduces electricity consumption costs by 32.54% compared to algorithms that only optimize the scheduling of loads or some components of the home energy management system.

Optimized configuration and operation model and economic analysis of shared energy storage based on master-slave game considering load characteristics of PV communities

As a new form of energy storage, shared energy storage (SES) is characterized by flexible use and high utilization rate, and its application in photovoltaic (PV) communities has not yet been promoted because of the unclear operation mode and revenue effect. This paper focuses on the configuration, operation and economic benefits of SES in PV communities, comparing the differences in electricity consumption behavior and cost of electricity in PV communities with and without SES scenarios. First, a trading framework between SES and PV community is constructed by combining the reality of China's power market. SES provides charging and discharging services as well as dynamic capacity rental service, and PV community chooses the capacity as well as the time to use SES according to its own situation. Then, the decision-making advantage of SES is considered, and a SES-PV community model based on the master-slave game is established, where the upper level optimizes the system capacity configuration and operation with the goal of minimizing the cost of SES, and the lower level optimizes the electricity consumption behavior with the goal of minimizing the cost of electricity consumption in the PV community. Finally, the adopted model is simulated to obtain the optimal configuration capacity, operation strategy of SES and load optimization results for PV community. The respective costs of SES and PV community are further measured. The results show that SES is only profitable with subsidies, and the cost of electricity in the PV community with SES is reduced by 17.16 % compared to the community without SES. In addition, a sensitivity analysis is conducted to address the benefits of SES, which can be realized through the use of more cost-effective construction materials, access to the electricity market, appropriately increasing the price of dynamic rental capacity, and selecting the operation in the area where the PV output and the community load are more compatible. The research can provide reference for the development mode of SES and the optimization of energy use in PV communities.

An optimal dispatch schedule of EVs considering demand response using improved MACD algorithm

The growing popularity of electric vehicles presents a significant challenge to current electric grids since the rising number of these vehicles places additional strain on power systems inside distribution networks. A proposed paradigm is presented for electric vehicles (EVs), which is subsequently partitioned into three distinct dispatching areas to assess its practicality. This study is structured around two primary objectives. The first objective focuses on EV owners aiming to minimize their electricity consumption costs while also receiving compensation for providing services. The second objective involves using an aggregator to establish distinct tariffs for each dispatching area. Additionally, the aggregator aims to shift the charging load demand from peak to off-peak hours and distribute the charging demand to each agent. The authors of this study propose the utilization of a charging and discharging coordination method, specifically the Multi-Agents Charging and Discharging (MACD) algorithm, as a means to successfully tackle the issue of charging demand during peak hours. The objective of the proposed algorithm is to effectively handle the increased charging requirements during peak periods by using vehicle-to-grid (V2G) technologies within the context of Smart Grid systems and electric vehicle (EV) batteries. Importantly, this reduction is achieved without compromising the performance of electric vehicles (EVs) or the convenience experienced by EV owners. The algorithm under consideration demonstrates reduced power charging costs for various sectors. Specifically, it achieves a decrease of 15% for households, 14.6% for corporate buildings, and 14.5% for Industrial Park EV aggregators.

Multi-objective global dynamic optimal scheduling of smart building loads considering carbon emissions

In recent years, there has been a significant increase in electricity consumption and carbon emissions from households in smart buildings. This increase has had a profound impact on the economic aspects of residential electricity consumption and the achievement of the national dual carbon goal. In this paper, an optimal scheduling model for household loads in smart buildings is established. It considers the user demand side, grid-side supply, and the impact of the PV energy storage system (PESS) on carbon emissions. The optimization objectives are the economy of household electricity consumption and the total deviation of electricity consumption. Second, the adaptive mixed inverse learning strategy multi-objective beluga optimization algorithm (AMOBWO) is adopted to enhance its overall dynamic optimization. This optimization addresses challenges such as the uneven distribution of electricity consumption, load timing constraints, power limitations, and uncertainty in electricity consumption during the scheduling of smart building loads. Finally, the results demonstrate that AMOBWO decreases the total cost of household electricity consumption in smart buildings by 59.32% (based on the selected maximum PV capacity). Additionally, it reduces the total deviation by 14.28% after optimal scheduling. As the PESS capacity increases, there is a significant reduction in the indirect carbon emissions from smart buildings, decreasing from 881.279 kg CO2 to 431.299 kg CO2.

Research on Optimal Operation of Substation Self-consumption Load Considering Uncertainty

Under the goal of "double carbon", in order to reduce the power consumption, reduce the cost of carbon emission and increase the utilization rate of renewable energy, we propose the optimal operation method of substation self-powered load considering uncertainty. The optimal operation model of substation is established with the objective function of minimizing the operating cost, carbon emission cost and load power variance. The optimal operation strategy of the substation is solved by the affiliation function and particle swarm algorithm, and at the same time, the starting and stopping situations of each load of the substation are solved according to integer planning. A substation in Henan Province is analyzed as an example, and the results show that the method has obvious advantages in reducing the cost of electricity consumption, carbon emission cost, and peak-to-valley difference of the power grid when considering different objectives for optimization comparison under the consideration of photovoltaic and time-sharing tariffs.

Assessment of Electricity Consumption Characteristic: Textile Factory Case Study

Currently, electrical energy tariffs are a crucial factor in the electricity market, as they significantly impact the decisions made by end users. They play a vital role in the effectiveness of energy management systems (EMS). Tariffs are not solely considered a fixed component of expenditure calculations. Instead, they are indirectly linked to the costs of power generation, electricity transmission, and electricity distribution, as well as other determinants such as government taxation. In certain regions, improper tariff calculation methodologies have resulted in substantial power losses, superluous investments, increased operating costs, and environmental pollution because of the underutilization of available renewable energy sources. This study examined the electrical energy consumption values and characteristics of an integrated textile factory. Additionally, analyses were conducted on the electricity tariffs published by the Energy Market Regulatory Authority (EMRA) Electricity Energy Market management, in order to decrease the electrical energy consumption costs of the textile factory. Based on the findings of the analyses, suggestions were put forward for regulating the electrical energy consumption characteristics and reducing the electrical energy consumption costs.

Assessment of Biomedical Waste Generation in Dialysis Units: A Prospective Observational Study-Is it Time for "Green Dialysis"?

Introduction: Chronic kidney disease and as a consequence end-stage kidney disease (EKSD) is increasing globally. More and more people across the world are requiring hemodialysis (HD). The HD procedure produces a large quantity of biomedical waste. In addition, HD consumes a large quantity of water. In this study, we estimated the waste generated from our government-funded HD unit. Materials and methods: It is a prospective study that was carried out in the dialysis unit in the nephrology department over a period of 1 year. The daily dialysis waste generated by the unit was measured using a spring balance. The proportion of plastic and nonplastic waste was determined. The quantity of biomedical waste generated per person in 1 year was calculated. Water input to the dialysis unit was noted. Water consumption per dialysis was calculated. Liquid chemical waste consumed was determined. Electricity consumed by the unit was measured by the electricity meter. The cost of waste disposal was calculated. The cost of electricity consumption and water consumption was also calculated. Results: The approximate weight of waste disposables generated in one dialysis was 0.75 kg. Approximately each person generates 1.29 kg of waste per dialysis. Each dialysis required 125 L of reverse osmosis (RO) water and to generate 125 L of RO water 250 L of raw water was used. This happens as 125 L of water are rejected during the generation of 125 L of RO water. Thus, the net water consumption for each dialysis was 250 L. Chemical waste generated per dialysis includes 90 mL citric acid per dialysis and 130 mL bleach. Each dialysis consumes 3 kWh (three units) of electricity. The cost of electricity for each dialysis was 25.5 INR and the cost of water was 25 INR per dialysis. The cost of waste disposal for each dialysis bed was 6 INR. Discussion: Each dialysis patient produced 1.29 kg of waste per dialysis which was like other studies. Unlike other studies, the waste was not being reprocessed or recycled. Conclusion: Hemodialysis produces substantial biomedical waste. Proper waste disposal techniques and policies to promote reduction, reuse, and recycling will go a long way toward promoting green dialysis and reducing environmental as well as economic burdens. How to cite this article: Sahay M, Sahay RK, Seshadri B, et al. Assessment of Biomedical Waste Generation in Dialysis Units: A Prospective Observational Study-Is it Time for "Green Dialysis"? J Assoc Physicians India 2023;71(10):49-52.

Sustainable deployment of autonomous vehicles dedicated lanes in urban traffic networks

Autonomous vehicles (AVs) show promise for increasing roadway safety and capacity. During the AV transition era, which will be characterized by a mixed fleet of AVs and human-driven vehicles (HDVs), it is expected that the allure of these prospective benefits will motivate road agencies to allocate AV-dedicated lanes. This paper proposes a sustainability-driven AV-dedicated lane and pricing policy (SALP) framework that addresses the three pillars of sustainable development—social, environmental, and economic. The framework is formulated as a bi-level problem where the upper-level model yields decisions on the timing, location, and quantity of AV-dedicated lanes and tolling levels to minimize total travel time, emissions, and electricity consumption costs (that is, the economic and environmental pillars). To alleviate potential inequity (the social pillar), two considerations are proposed: revenue neutrality to compensate for the increase in travel costs of travelers and an equity constraint to limit the exacerbation of HDV travel costs. At the lower level, travelers react to the decisions made at the upper level by choosing their vehicle types (AV vs. HDV) and routes. The SALP is solved using Genetic and Frank-Wolfe algorithms. The results of the numerical experiments suggest that the proposed SALP addresses all three pillars, as it yields significant reductions in total travel time, emissions, and electricity costs.

Efficient Demand Side Management Using a Novel Decentralized Building Automation Algorithm

Given its adaptable and efficient energy consuming devices during peak hours, the residential building sector is urged to take part in demand response (DR) initiatives with the use of a building energy management system (BMS). The residents of buildings with BMS enjoy secure, pleasant, and fully managed lifestyles. Although the BMS helps the building consume less energy and encourages occupant engagement in energy-saving initiatives, unwelcome interruptions and harsh instructions from the system are inconvenient for the inhabitants, which further discourages their participation in DR initiatives. Building automation control is a crucial factor for improving buildings’ energy efficiency and management, as well as improving the electricity grid’s reliability indices. Smart houses that use the right sizing procedure and energy-management techniques can help lower the demand on the entire grid and potentially sell clean energy to the utility. Recently, smart houses have been presented as an alternative to traditional power-system issues including thermal plant emissions and the risk of blackouts brought on by malfunctioning bulk plants or transmission lines. This paper describes the necessary technology requirements and presents the methodology and the decentralized building automation novel algorithm for efficient demand side management in a building management system. Human comfort aspects including thermal comfort and visual comfort were taken into consideration when selecting heating and lighting controls. The suggested BMS relies primarily on a load-shifting technique, which moves controllable loads to low-cost periods to avoid high loading during peak hours. The model aims to minimize the individual household electricity consumption cost while considering customers’ comfort and lifestyle. All these are applied in an experimental university microgrid, and the results are presented in terms of energy saving in kWh, money in €, and working hours. The results demonstrated that the proposed approach might successfully lower energy use during the DR period and enhance occupant comfort.

Efficient Techniques for Residential Appliances Scheduling in Smart Homes for Energy Management Using Multiple Knapsack Problem

The evolution of the smart grid has enabled residential users to manage the ever-growing energy demand in an efficient manner. The smart grid plays an important role in managing this huge energy demand of residential households. A home energy management system enhances the efficiency of the energy infrastructure of smart homes and provides an opportunity for residential users to optimize their energy consumption. Smart homes contribute significantly to reducing electricity consumption costs by scheduling domestic appliances effectively. This residential appliance scheduling problem is the motivation to find an optimal appliance schedule for users that could balance the load profile of the home and helps in minimizing electricity cost (EC) and peak-to-average ratio (PAR). In this paper, we have focused on appliance scheduling on the consumer side. Two novel home energy management models are proposed using multiple scheduling options. The residential appliance scheduling problem is formulated using the multiple knapsack technique. Serial and parallel scheduling algorithms of home appliances namely MKSI (Multiple knapsacks with serial implementation) and MKPI (Multiple knapsacks with parallel implementation) are proposed to reduce electricity cost and PAR. Price-based demand response techniques are incorporated to shift appliances from peak hours to off-peak hours to optimize energy consumption. The proposed algorithms are tested on real-time datasets and evaluated based on time of use pricing tariff and critical peak pricing. The performance of both the algorithms is compared with the unscheduled scenario and existing algorithm. Simulations show that both proposed algorithms are efficient methods for home energy management to minimize PAR and electricity bills of consumers. The proposed MKSI algorithm achieves cost reduction of 20.26% and 42.53% for TOU and CPP, respectively as compared to the unscheduled scenario while PAR is reduced by 45.07% and 39.51% for TOU and CPP, respectively. The proposed MKPI algorithm achieves 22.33% and 46.36% cost reduction compared to the unscheduled case for TOU and CPP while the PAR ratio is reduced by 46.47% and 41.16% for TOU and CPP respectively.