Electricity Cost Savings Research Articles

Empirical study of facade retrofits for optimizing energy efficiency and cooling in school buildings in Saudi Arabia

Educational buildings in Saudi Arabia are characterized by high energy consumption, primarily due to cooling and lighting demands. This study aims to evaluate and optimize facade retrofitting strategies to improve energy efficiency and evaluate economic performance of many scenarios in Saudi school buildings, aligning with the sustainability objectives of Saudi Vision 2030. The research employs a simulation-based optimization methodology utilizing Design Builder software to explore key facade design variables, including Window-to-Wall Ratio, glazing type, local shading devices, and external wall insulation. The methodology is structured in sequential stages: it begins with the collection of base-case data, followed by a data-driven optimization process targeting specific design variables. This is then complemented by an economic and environmental assessment of the optimal retrofit scenarios, ultimately leading to the identification of the most effective energy-saving solution presented at the conclusion of the study. Through an optimization process, the research identifies retrofitting strategies that reduce cooling loads by up to 17 %, lighting energy consumption by 49 % and annual energy costs by 18 %. The study also includes an economic analysis of the retrofits, revealing significant savings in annual electricity costs and a reduction in the building's carbon footprint. This research contributes valuable insights into sustainable building practices, offering practical solutions for improving energy efficiency in the educational sector. The novelty of this study lies in its comprehensive analysis of facade retrofits within the context of extreme climatic conditions, which provides a replicable model for retrofitting educational buildings across the region. The findings have significant implications for policymakers, architects, and sustainability advocates seeking to meet Saudi Arabia's energy conservation goals.

Effects of occupant thermostat preferences and override behavior on residential demand response in CityLearn

As space heating accounts for 54% of annual residential electricity consumption in Quebec, demand response programs specifically target load shifting through the automated control of thermostat setpoints during peak hours. On a district scale, varied thermostat preferences and setpoint override behaviors can have an impact on the success of the demand response program. This study examines two unique occupant types (Average, Tolerant) in terms of thermostat setpoint preferences as well as three different occupant Levels-of-Detail (LoDs) and analyzes their effects on the energy flexibility provided during demand response periods. For our baseline scenario, LoD 1, a static setpoint schedule is used and there is no control of the heat pump, while LoD 2 and LoD 3 incorporate thermostat setbacks during demand response events. LoD 2 assumes the occupant is comfortable within 2°C from the setpoint while LoD 3 allows the occupant to override the DR setbacks. We estimate the flexibility services provided by a ten-house residential community through the automated control of heat pumps during a three-month winter period, and we implement and simulate our study in CityLearn using reinforcement learning-based control for district-level energy management. When comparing LoD 3 to LoD 1, electricity cost was reduced by approximately 12% and net electricity consumption was reduced by approximately 17% during demand response periods. Likewise, we find that LoD 2 could overestimate savings in net electricity consumption, cost, and peak demand by 5% compared to LoD 3. The number of hours where the indoor temperature deviated more than ▪ from the setpoint occurred for less than 5% of the timesteps on average for the 10 buildings for LoD 3 while still achieving significant net electricity consumption reductions, thus highlighting that we can provide energy flexibility services to the grid while balancing occupant thermal comfort. Finally, the agents learned optimal decision-making that reduced the number of overrides across the training episodes for both Average and Tolerant occupants. We thus present a multi-agent framework as a means for addressing various occupant setpoint preferences and override behaviors for the control of heat pump systems at the neighborhood level.

Optimal Scheduling of Off-Site Industrial Production in the Context of Distributed Photovoltaics

A reasonable allocation of production schedules and savings in overall electricity costs are crucial for large manufacturing conglomerates. In this study, we develop an optimization model of off-site industrial production scheduling to address the problems of high electricity costs due to the irrational allocation of production schedules on the demand side of China’s power supply, and the difficulty in promoting industrial and commercial distributed photovoltaic (PV) projects in China. The model makes full use of the conditions of different PV resources and variations in electricity prices in different places to optimize the scheduling of industrial production in various locations. The model is embedded with two sub-models, i.e., an electricity price prediction model and a distributed photovoltaic power cost model to complete the model parameters, in which the electricity price prediction model utilizes a Long Short-Term Memory (LSTM) neural network. Then, the particle swarm optimization algorithm is used to solve the optimization model. Finally, the production data of two off-site pharmaceutical factories belonging to the same large group of enterprises are substituted into the model for example analysis, and it is concluded that the optimization model can significantly reduce the electricity consumption costs of the enterprises by about 7.9%. This verifies the effectiveness of the optimization model established in this paper in reducing the cost of electricity consumption on the demand side.

Thermal energy storage performance of biaxial voided RCC roof slab integrated with macroencapsulated PCM for passive cooling of buildings

Building space heating and cooling power consumption is growing due to population and economic growth. Integrating phase change materials (PCMs) into various building envelope components is being explored to enhance thermal energy storage capacities. Specifically, roofs exposed to direct sunlight significantly promote thermal energy transfer to the interior, increasing the space heating and cooling requirements. In this study, thermal energy storage performance of a biaxial voided roof slab integrated with PCM is experimentally investigated under ambient conditions and compared with a normal reinforced concrete (RCC) without PCM. PCM selection, characterization, and metal void former development are performed, followed by macroencapsulation and integration into the roof slab. Thermal performance measures are evaluated, including temperature profile, heat flux, thermal load, time lag, and decrement factor. Financial viability indicators such as electricity cost savings, payback period, and CO2 emissions savings are also assessed. The results show PCM integrated biaxial voided roof slab reduces interior temperature by upto 7.2 °C during sunny hours, heat transfer to the interior by upto 60.6 %, and thermal load by upto 54 %. In addition, considering heating and cooling, it offers an average daily saving of 0.06 USD/kWh/m2 and a payback period of about 5.7 years. Further, it gives CO2 emissions savings of upto 13.7, 12.3, and 4.3 kgCO2/kWh for lignite-, coal-, and natural gas-fired power plants. Furthermore, its mean time lag is 4.2 h, with a decrement factor of 0.75 compared to 3.9 h and 0.85 for the normal RCC unit. The study makes significant contribution to knowledge by providing a unique, simple yet highly effective PCM macroencapsulation integration technique for roof slabs and insights into its thermal performance under the hot weather conditions.

Interventional Imaging Systems in Radiology, Cardiology, and Urology: Energy Consumption, Carbon Emissions, and Electricity Costs.

BACKGROUND. The energy demand of interventional imaging systems has historically been estimated using manufacturer-provided specifications rather than directly measured. OBJECTIVE. The purpose of this study was to investigate the energy consumption of interventional imaging systems and estimate potential savings in the carbon emissions and electricity costs of such systems through hypothetical operational adjustments. METHODS. An interventional radiology suite, neurointerventional suite, radiology fluoroscopy unit, two cardiology laboratories, and two urology fluoroscopy units were equipped with power sensors. Power measurement logs were extracted for a single 4-week period for each radiology and cardiology system (all between June 1, 2022, and November 28, 2022) and for the 2-week period from July 31, 2023, to August 13, 2023, for each urology system. Power statuses, procedure time stamps, and fluoroscopy times were extracted from various sources. System activity was divided into off, idle (no patient in room), active (patient in room for procedure), and net-imaging (active fluoroscopic image acquisition) states. Projected annual energy consumption was calculated. Potential annual savings in carbon emissions and electricity costs through hypothetical operational adjustments were estimated using published values for Switzerland. RESULTS. Across the seven systems, the mean power draw was 0.3-1.1, 0.7-7.4, 0.9-7.6, and 1.9-12.5 kW in the off, idle, active, and net-imaging states, respectively. Across systems, the off state, in comparison with the idle state, showed a decrease in the mean power draw of 0.2-6.9 kW (relative decrease, 22.2-93.2%). The systems had a combined projected annual energy consumption of 115,684 kWh (range, 3646-26,576 kWh per system). The systems' combined projected energy consumption occurring outside the net-imaging state accounted for 93.3% (107,978/115,684 kWh) of projected total energy consumption (range, 89.2-99.4% per system). A hypothetical operational adjustment whereby all systems would be switched from the idle state to the off state overnight and on weekends (versus being operated in idle mode 24 hours a day, 7 days a week) would yield the following potential annual savings: for energy consumption, 144,640 kWh; for carbon emissions, 18.6 metric tons of CO2 equivalent; and for electricity costs, US$37,896. CONCLUSION. Interventional imaging systems are energy intensive, having high consumption outside of image acquisition periods. CLINICAL IMPACT. Strategic operational adjustments (e.g., powering down idle systems) can substantially decrease the carbon emissions and electricity costs of interventional imaging systems.

Revolutionizing Demand Response Management: Empowering Consumers through Power Aggregator and Right of Flexibility

This paper introduces a groundbreaking approach to demand response management, aiming to empower consumers through innovative strategies. The key contribution is the concept of “acquiring flexibility rights”, wherein consumers engage with power aggregators to curtail energy usage during peak-load periods, receiving incentives in return. A flexibility right coefficient is introduced, allowing consumers to tailor their participation in demand response programs, ensuring their well-being. Additionally, a lighting intensity control system is developed to enhance residential lighting network efficiency. The study demonstrates that high-energy consumers, adopting a satisfaction factor of 10, can achieve over 61% in electricity cost savings by combining the lighting control system and active participation in demand response programs. This not only reduces expenses but also generates income through the sale of flexibility rights. Conversely, low-energy consumers can fully offset their expenses and accumulate over USD 33 in earnings through the installation of solar panels. This paper formulates an optimization problem considering flexibility rights, lighting control, and time-of-use tariff rates. An algorithm is proposed for a distributed solution, and a sensitivity analysis is conducted for evaluation. The proposed method showcases significant benefits, including cost savings and income generation for consumers, while contributing to grid stability and reduced blackout occurrences. Real data from a residential district in Tehran validates the method’s effectiveness. This study concludes that this approach holds promise for demand response management in smart grids, emphasizing the importance of consumer empowerment and sustainable energy practices.

To charge or not to charge? Using Prospect Theory to model the tradeoffs of electric vehicle users

AbstractElectric vehicle (EV) users who aim to become flexibility providers face a tradeoff between staying in control of charging and minimizing their electricity costs. The common practice is to charge immediately after plugging in and use more electricity than necessary. Changing this can increase the EV’s flexibility potential and reduce electricity costs. Our extended electricity cost optimization model systematically examines how different changes to this practice influence electricity costs. Based on the Prospect Theory and substantiated by empirical data, it captures EV users’ tradeoff between relinquishing control and reducing charging costs. Lowering the need to control charging results in disproportionally large savings in electricity costs. This finding incentivizes EV-users to relinquish even more control of charging. We analyzed changes to two charging settings that express the need for control. We found that changing only one setting offsets the other and reduces its positive effect on cost savings. Behavioral aspects, such as rebound effects and inertia that are widely documented in the literature, support this finding and underline the fit of our model extension to capture different charging behaviors. Our findings suggest that service providers should convince EV-users to relinquish control of both settings.

Performance Assessment of Solar Ice Slurry Cold Storage System for Solar Refrigerated Vehicle

This article mainly analyzes the ice slurry cold storage system in solar refrigerated vehicles, calculates and studies the thermal properties of the ice slurry, the cold load of the refrigerated vehicle, and the solar photovoltaic panels, and calculates the fuel savings of using solar power generation. Study the demand for ice slurry in ice slurry storage systems under different energy utilization efficiencies, analyze the economy of solar ice slurry generation systems, and calculate the recovery period of the system in this paper. The daily power generation of solar photovoltaic panels can run the electric motor for 2.93 hours. The solar refrigerated truck can save 7.91 liters of oil per day and reduce carbon dioxide emissions by 1.25×107 liters per day. The average annual electricity cost savings are 4026.67 CNY. Based on the total cost of the photovoltaic energy storage hybrid quality system, it will take 3.89 years to recover the cost. Calculate the density and enthalpy of the ice slurry based on the thermophysical model of the ice slurry, and provide different utilization amounts for the ice slurry mass generated by the ice slurry generation system at different efficiencies. Calculate the total weight of the ice slurry system.

Stambena zajednica u ulozi kupca-proizvođača

Prosumers have been recognized as the electricity system users and the electricity market participants since 2021 by the regulations of the Republic of Serbia. Among others, households, both in individual and collective housing facilities, are given the opportunity to acquire prosumer status, which enables them to achieve savings in monthly electricity costs and contribute to reducing environmental pollution and slowing down global warming. In this paper, residential communities were analysed as prosumers. The method of acquiring prosumer status for residential communities is described, starting from the basic idea, through the regulation of mutual relations between the members of the residential community and the conclusion of the supply contract to the construction and connection of the production facility to the power system. In addition, the method of electricity billing for the prosumer that is a residential community is shown in the paper. As a conclusion, proposals for additional savings for prosumers who are residential communities were given, as well as the models for improving their status.

Electricity cost and CO2 savings potential for chlor-alkali electrolysis plants: Benefits of electricity price dependent demand response

Chlor-alkali electrolysis plays a significant role in Germany's electricity demand, with a share of >2%. It offers a promising avenue for leveraging demand response strategies. In times of escalating electricity prices, load shifting can help to maintain economic competitiveness of domestic industries. This study aims to assess the potential for electricity cost savings and related CO2 emission reductions through optimal load shifting practices for chlor-alkali electrolysis, considering both current and projected future electricity prices. The research employs the mixed-integer energy system modeling framework IRPopt to model and optimize a chlorine value chain under various scenarios. A sensitivity analysis is conducted to identify the primary factors driving electricity cost savings. The findings reveal that, based on the 2019 electricity price distribution (before pandemic and energy crisis), load shifting can yield electricity cost savings of 5.8% and CO2 emission reductions of 2.7%. As the share of variable renewable energy sources increases along with other driving factors, the electricity price spread is projected to rise from 28% in 2019 to an average of 87% in 2040 scenarios. Consequently, electricity cost savings rise up to 22% and CO2 emission reductions up to 10%. The primary drivers behind electricity cost savings include electricity price spreads and the utilization and operating range of the electrolyzer. In addition to the business-level advantages, the resulting decrease in residual load and lower CO2 emissions contribute to the integration of variable renewable energy sources and the achievement of decarbonization targets at the economic level.

Flexibility options in a decarbonising iron and steel industry

The decarbonisation of the iron and steel industry is expected to significantly increase its electricity consumption due to higher levels of electrification and the partial shift to hydrogen as iron reductant. With its batch processes, this industry offers large potential for the application of demand response strategies to achieve electricity cost savings. Previous research has primarily focused on investigating the demand response potential for currently operating manufacturing processes and partly for future low-carbon processes. This study aims to consolidate this knowledge and apply it to a modelling analysis that investigates the demand response potential of two new low-carbon technologies: the hydrogen-based direct reduction of iron with electric arc furnace technology (H2-DRI-EAF) and the blast furnace basic oxygen furnace technology retrofitted with carbon capture (BF-BOF-CCUS). A cost optimisation approach is applied to plant configurations with varying parameters relevant for flexibility, such as electrolyser and storage sizes, and in the context of future electricity prices. Multiple price profiles are selected to encompass uncertainties on the development of the power system. The potential for a H2-DRI-EAF plant is 3–27 times higher than for a BF-BOF-CCUS, with electricity costs savings potentials of 35% and 3%, respectively. The study finds that electricity prices have the most significant impact on the profitability of investing in electrolyser overcapacities, which enable operating costs reduction. Therefore, the profitability of these investments are strongly dependent on future power system configurations.

Flexible Operation to Reduce Greenhouse Gas Emissions along the Cold Chain for Chilling, Storage, and Transportation—A Case Study for Dairy Products

The further expansion of renewable energies in Germany requires flexible consumers to balance fluctuations in electricity production from variable renewable energies. Cold storage warehouses, due to their inherent storage capacity and widespread use, are well-suited for integrating more renewable energies. The potential of cold storage warehouses is often viewed in isolation and not in conjunction with the cold chain’s upstream and downstream processes. By adjusting the temperatures within the processes, the individual links in the cold chain can be made flexible. To assess the effects of flexibilization on emissions and electricity costs, thermodynamic models of the individual links in the cold chain and of a yogurt pallet are developed and linked together. Due to temperature fluctuations in the products resulting from the flexibilization, emission evaluations must be considered throughout the cold chain. Results of the simulation for the study period show that emissions reductions and electricity cost savings can be achieved in all three links when they are made flexible. However, the savings vary in magnitude. Only minor savings can be achieved in the cooling tunnel. The greatest potential for savings is in refrigerated transport, if deeper cooling occurs in the process before, i.e., in the cold storage warehouse.

Oxygen Demand Forecasting and Optimal Scheduling of the Oxygen Gas Systems in Iron- and Steel-Making Enterprises

Due to the imbalance between the supply and demand of oxygen, the oxygen systems of iron- and steel-making enterprises in China have problems with high oxygen emissions and high pressure in the pipelines, resulting in the energy consumption of oxygen production being high. To reduce the energy consumption of oxygen systems, this study took a large-scale iron- and steel-making enterprise as a case study and developed a two-stage forecasting and scheduling model. The novel aspect and progressiveness of this work are as follows: First, an oxygen demand forecasting model was developed based on the backpropagation neural network with genetic algorithm optimization (GABP) and is driven only by historical data. Compared with some complex models in the literature, although the accuracy of this model has been reduced, the model does not need to consider production plans for other process steps, making it more practical and feasible. Second, different from the existing literature, an oxygen production scheduling model was developed for load-variable ASUs with an internal compression process, and both the oxygen emissions and pipeline pressure are included in the objective function. The case study showed that based on the oxygen demand forecast and optimal scheduling, the oxygen emissions and pipeline pressure in the studied iron- and steel-making enterprise can be significantly reduced, thereby achieving considerable energy-saving effects and economic benefits. Specifically, the following conclusions were obtained: (1) For the oxygen demand forecast, the prediction accuracy of the GABP model was better than that of the ARIMA model. The average MAPE of the 12 sets of data of the ARIMA and GABP models was 23.8% and 20.2%, respectively. (2) By comparing the scheduling results and the field data, it was found that after scheduling, the amount of oxygen emissions decreased by 6.32%, the pipeline pressure decreased by 0.61%, and the energy consumption of oxygen compression decreased by 1.6%. Considering both the oxygen emission loss and the energy consumption of oxygen compression, the total power consumption of the studied oxygen system was reduced by 1.38%, resulting in electricity cost savings of approximately 9.03 million RMB per year.

Optimal Battery Energy Storage Control for Multi-Service Provision Using a Semidefinite Programming-Based Battery Model

Battery energy storage systems (BESS) capable of simultaneously providing multiple grid services can assist a distribution grid operator in overcoming various challenges due to the high penetration of distributed solar photovoltaics and accelerated electrification. The seamless provision of multiple services can be ensured with BESS control decisions based on an accurate model reflecting the battery characteristics. Thus, this paper presents a novel Li-ion battery model based on linear matrix inequalities along with a semidefinite programming (SDP)-based model to determine the optimal BESS control decisions for the provision of multiple services in distribution systems. Peak shaving, power factor improvement, and electricity cost savings are considered for the services. Moreover, the mathematical analysis reveals that the original nonconvex problem can be equivalently transformed into the SDP model. The effectiveness of using BESS in the distribution system is verified through simulations using real-world data. Ultimately, a comparative analysis with a conventional linear model demonstrates that the proposed battery model reduces the energy losses in the batteries, in the simulation condition, and help maintain the battery states within normal operating limits.

Performance Evaluation of High-Rise Buildings Integrated with Colored Radiative Cooling Walls in a Hot and Humid Region

Radiative sky cooling is an appealing form of heat exchange between terrestrial objects and outer space through thermal radiation, which is attracting worldwide interest due to its nature as passive cooling, that is, cooling without consuming energy. Due to a recent breakthrough in material science, sub-ambient daytime radiative sky cooling has been effectively achieved, which has significantly stimulated research interest in this field. In view of the numerous radiative coolers being reported as having excellent spectral properties and cooling ability under sunlight, integrating these superb cooling materials into building skins is a promising route to implementing radiative sky cooling technology. To this end, this study deploys state-of-the-art colored radiative cooling coatings as a new retrofitting strategy for building walls, and then conducts a comprehensive performance evaluation by considering a high-rise building situated in the hot-humid city of Hong Kong. Potential benefits of implementing differently colored cooling wall strategies, including their performance regarding thermal insulation, energy savings, economic viability, and environmental sustainability, were thoroughly investigated. The obtained results elucidate that for the utilization of the porous P(VdF-HFP)-based bilayer wall, relative to the monolayer, the frequency of the wall temperature exceeding the surrounding environment on an annual basis can be further reduced by up to 4.8%, and the yearly savings in cooling electricity vary from 855.6 to 3105.6 kWh (0.4–1.5%) with an average of 1692.4 kWh. Besides this, the yearly savings in net electricity cost vary from 1412.5 to 5127.3 HKD and the reduction in carbon emissions ranges from 1544.4 to 5606.1 kg with an average of 3055.0 kg. In addition, discussions of the combination of the super-cool roof strategy with blue porous polymer-based cooling walls reveal that the achievable savings in terms of energy costs and reductions in carbon emissions are 1.6 and 2.2 times more than either the application of the super-cool roof or porous polymer bilayer walls alone, respectively. This research offers new understandings of the deployment of colored cooling coatings on vertical building façades in hot and humid regions, which can considerably facilitate the realization of low-energy buildings in a passive approach for stakeholders.

Impact of gypsum mortars functionalized with phase change materials in buildings

The construction industry needs to adopt raw materials with less environmental impact and functional raw materials. The utilization of phase change materials (PCM) in interior mortars allows the energy efficiency of buildings improvement, based on solar energy, a renewable and clean energy source. On the other hand, the selection of mortars based in binders whose production emits lower carbon dioxide emissions than cement, like gypsum, can be a promising solution in help to minimize carbon dioxide (CO2) emissions and the energy consumption for raw materials production. The main objective of this work was the development and characterization of gypsum-based mortars functionalized with PCM. Four distinct mortars were developed with different contents of pure and non-encapsulated PCM (0 %, 5 %, 10 % and 20 %). The different mortars were tested from a physical and mechanical point of view. The thermal performance of the mortars according to the summer and spring seasons of the north part of Portugal and an economic analysis of the application of these mortars in a typical Portuguese building were also carried out.The results obtained allow to observe that the incorporation of 20 % PCM leads to a decrease in the water absorption by capillarity, water absorption by immersion, flexural and compressive strengths of about 65 %, 30 %, 47 % and 59 %, respectively. However, its applicability has not been compromised. The thermal performance of the PCM mortars was improved, based in the temperature range and heating and cooling needs decrease. Regarding to the summer season it was observed a maximum temperature decrease of 2.3 °C, a minimum temperature increase of 2.6 °C and a decrease of cooling and heating needs of 13 % and 21 %, respectively. Concerning to the spring season it was verified a maximum temperature decrease of 2.7 °C, a minimum temperature increase of 1.9 °C and a decrease of cooling and heating needs of 100 % and 16 %, respectively. The cost analysis revealed savings in electricity costs for buildings climatization of around 17 % during the summer season and around 59 % during the spring season.

ENERGY AUDIT ON LIGHTING AND AIR CONDITIONING SYSTEMS AT AL-AZHAR ISLAMIC SCHOOL

Based on Government Regulation No. 70 of 2009 concerning energy conservation, it is stated that the use of energy by energy sources and users must be carried out sparingly and efficiently. Based on these regulations, this thesis was made with the aim of analyzing opportunities for saving electrical energy in the Islamic Middle School Al-Azhar 17 Pontianak and Islamic High School Al-Azhar 10 Pontianak buildings. Data collection was carried out from December 2022 to January 2023. The results of the analysis obtained 2 recommendations for saving electricity. In the first option, for the lighting system it is done by changing TL lamps to LEDs, requiring 240 LED lamps and for the air conditioning system it is done by changing the refrigerant to Musicool MC-22. Estimated electricity savings of 17%. The payback period is 2 years, 3 months. In the second option, 446 LED lamps were also replaced with TL lamps to LEDs. For the air conditioning system, the AC is replaced with an inverter type. Estimated electricity savings of 61.13%. The payback period is 2 years, 2 months. From the results of this analysis, energy conservation efforts to get electricity savings and cost savings are very feasible in these schools.

Modelling renewable energy communities: assessing the impact of different configurations, technologies and types of participants

BackgroundEnergy communities (ECs) have emerged as a solution to support governments mitigating climate change and comply with decarbonization goals, while introducing end-users on the energy value chain. In this paradigm, citizens have an active role in reducing electricity demand from the utility grid, by generating, sharing and/or trading locally generated renewable energy, such as solar energy. However, the economic and environmental outputs of energy communities are dependent on a variety of factors, such as technology features (renewable energy generation, existence of flexible equipment and/or energy storage systems), types of participants (consumers and prosumers with different electricity intensity and load profiles), and electricity sharing/trading agreements. As such, assessing the impact these will have on delivering benefits to the energy community and its participants is of paramount importance.MethodsThis work models different energy communities’ design typologies in Lisbon, Portugal considering different types of consumers with heterogenous electricity demand profiles and willingness to participate, multiple technology deployment scenarios (solar systems installation, batteries, and electric vehicles), and electricity trading (collective self-consumption versus peer-to-peer trading).ResultsResults demonstrate community electricity cost savings are up to 42%, with self-sufficiency rate up to 12.5%, which is considerably low due to the participation of high demanding sectors (such as industry or retail). At participants’ individual level, electricity costs savings can reach 48% and 53%, for residential consumers and prosumers, respectively, while for high-demanding participants are slightly lower: 43% for hotel, 44% for retail, 13% for industry and 5% for university. Individual self-sufficiency rates register highest results for the residential prosumers (35% for PV prosumers, 28% for PV + electric vehicles and 54% with PV + batteries) while for other participants results fall between 6% (retail) and 26% (industry).ConclusionsWe conclude that for ECs deployment, individual PV self-consumption assets are not sufficient, thus greater PV sizes and higher adoption rates should be considered, according to consumer and prosumers shares. The share/trade of PV surplus, paired with competitive aggregation tariffs results in positive economic and environmental outputs, for the majority of both consumers and prosumers.

A novel hybrid Harris hawk optimization and sine cosine algorithm based home energy management system for residential buildings

Smart grid technology has given users the ability to regulate their home energy in a much more effective manner. In such scenarios, Home Energy Management (HEM) potentially becomes an arduous task, as it necessitates the optimal scheduling of smart appliances in order to reduce energy usage. In this research, a hybrid Harris Hawk Optimization-Sine Cosine Algorithm (hHHO-SCA) has been proposed to develop a meta-heuristic-based HEM system. The hybridization of HHO with SCA has been done to enhance the exploration and exploitation stages of HHO, hence improving its global search phase and effectively optimizing the energy usages. In addition to this, several knapsacks are utilized to guarantee that load demand for power users does not surpass a certain level during the peak hours. In terms of electricity prices and Peak to Average Ratio (PAR) reduction, the hybridization is demonstrated to be beneficial in achieving the objectives. Simulations are performed for a multi-family housing complex with a range of smart equipment. The results achieved with the proposed approach suggest that hHHO-SCA has been comparatively efficient in terms of cost reduction, and PAR, when compared to the other optimization techniques. Practical Application. This home energy management system can be applied to optimally schedule all the smart appliances in a building to minimize electricity consumption and provide the consumer with potential savings in electricity costs.

Experimental evaluation of daylighting performance and energy output of building-integrated photovoltaic (BIPV) panel in Bandung, Indonesia

Building energy use contributes up to 40% of total global energy use and increases by 8-10% every five years, encouraging the development of technology-based renewable energy sources. Building-Integrated Photovoltaic (BIPV) is a potentially relevant application of integrated PV in buildings which, it provides electricity cost savings, and increases the architectural attractiveness of a building. In contrast to conventional PV installations on building roofs, vertical PV installations are significantly influenced by the building orientation and the transparency of the PV. Study and analysis were conducted to implement BIPV windows in tropical climates using experimental measurement methods in energy output and indoor daylighting in four cardinal directions. This research was done by modelling a simple 2 × 2 × 1 m3 room with a PV solar window glazing made of Monocrystalline silicon 105Wp, placed on the roof of CADL Building, Institut Teknologi Bandung, Indonesia, for four days. The measurement results indicate that South is the most recommended orientation for installing BIPV windows for tropical areas in Bandung with the performance indicators for energy output = 564 Wh, DA300 = 100%, UDI250-750 = 93%, UDI100-3000 = 100%, and UDI>3000 = 0%. In contrast, West and East orientations are not recommended because of the significant difference in energy output and illuminance in the morning and the afternoon.