Reduce Electricity Costs Research Articles

Peer-to-peer trading in smart grid with demand response and grid outage using deep reinforcement learning

With the price of green energy now more reasonable, users can now produce enough electricity to meet their needs and make a profit by selling the surplus on the underground P2P energy market. For households, energy trading and demand management can reduce electricity costs. However, consumers generally obtain market offers based on their expectations and the forecasts of other households. However, the P2P exchange system is not able to quantify the gap between these offers and the best market. The objective of this paper is to apply deep reinforcement learning techniques to optimal energy trading and demand response (DR) methods within a peer-to-peer (P2P) market. The main objective is to maximize cost reductions. The best approach to achieve this objective was investigated as part of this project. The complexity of domestic energy trading and energy recovery is formally characterized as a partially observable Markov decision process (POMDP). Through decentralized training and performance-based learning, the strategy maximizes policy and value functions. In order to identify the most effective proactive solutions, a comparative analysis is carried out between the two parties. Based on the simulation results, it was observed that implementing the recommended reinforcement learning strategy to optimize peer-to-peer (P2P) energy exchange can lead to a significant improvement in the average household reward. Specifically, the average household reward can be increased by 7.6% and 12.08% by employing the aforementioned approach.

Study of Technological Advancement and Challenges of Façade System for Sustainable Building: Current Design Practice

Currently, several façade systems exist to enable sustainable building design. The biggest challenges for façade designers are to identify new technology and effective, sustainable systems that enable high structural and sustainable performance while producing a good aesthetic. Therefore, this paper aims to review the performance of existing façade systems for sustainable building designs and their limitations. Among modern façade systems, Double Skin Façades (DSF) show promise for energy efficiency, indoor air quality, and aesthetics. However, they face challenges like sound transmission between floors, higher initial costs, and outer skin vibrations. Furthermore, adaptive façades gained popularity for their active methods of achieving energy performance and comfort benefits but encountered complexities in design and construction, demanding codes and standards. Green wall systems enhance air quality and aesthetics, while photovoltaic façade systems reduce electricity costs, but both systems face higher initial costs and maintenance challenges. The review indicates that to produce a sustainable building design, architects, engineers, and builders must consider a sustainable façade system that enables high energy efficiency, less cost, better occupant comfort, and fewer environmental impacts.

A Multi-Objective Improved Cockroach Swarm Algorithm Approach for Apartment Energy Management Systems

The electrical demand and generation in power systems is currently the biggest source of uncertainty for an electricity provider. For a dependable and financially advantageous electricity system, demand response (DR) success as a result of household appliance energy management has attracted significant attention. Due to fluctuating electricity rates and usage trends, determining the best schedule for apartment appliances can be difficult. As a result of this context, the Improved Cockroach Swarm Optimization Algorithm (ICSOA) is combined with the Innovative Apartments Appliance Scheduling (IAAS) framework. Using the proposed technique, the cost of electricity reduction, user comfort maximization, and peak-to-average ratio reduction are analyzed for apartment appliances. The proposed framework is evaluated by comparing it with BFOA and W/O scheduling cases. In comparison to the W/O scheduling case, the BFOA method lowered energy costs by 17.75%, but the ICSA approach reduced energy cost by 46.085%. According to the results, the created ICSA algorithm performed better than the BFOA and W/O scheduling situations in terms of the stated objectives and was advantageous to both utilities and consumers.

Reduction in Residential Electricity Bill and Carbon Dioxide Emission through Renewable Energy Integration Using an Adaptive Feed-Forward Neural Network System and MPPT Technique

Increasing electricity demand and the emergence of smart grids have given home energy management systems new potential. This research investigates the use of an artificial neural network algorithm for a home energy management system. The system keeps track of and organizes the use of electrical appliances in a typical home with the objective of lowering consumer electricity bills. An artificial-neural-network-based maximum-power-point-tracking scheme is applied to maximize power generation from photovoltaic sources. The proposed neural network senses solar energy and calculates load requirements to switch between solar and grid sources effectively. The implementation of improved source utility does not require numerical calculations. Traditional relational operator techniques and fuzzy logic controllers are compared with the suggested neural network. The model is simulated in MATLAB, and the results show that the artificial neural network performs better in terms of source switching following load demand, with an operating time of less than 2 s and a reduced error of 0.05%. The suggested strategy reduces electricity costs without affecting consumer satisfaction and contributes to environmental friendliness by reducing CO2 emissions.

An evaluation of the demand response potential of integrated dynamic window and HVAC systems

Demand response (DR) increases the flexibility and reliability of the electricity grid as use of intermittent renewable energy sources increases. HVAC and envelope DR measures present the largest aggregate energy and peak demand savings potential of all commercial building end uses because their net demand savings occur during critical peak demand periods. Controllable envelope measures include switchable electrochromic windows, operable window attachments such as outdoor louvers, roller shades, and awnings, as well as other innovative facade technologies that can modulate both solar heat gain and daylight admission over a broad solar-optical range. This study evaluated the technical potential of DR-enabled dynamic windows to reduce critical peak demand for a prototypical medium office building situated in all 16 U.S. climates. Model predictive control (MPC) algorithms were designed to minimize electricity cost in daylit perimeter office zones through control of an electrochromic window with and without HVAC thermostat setpoint control. Conventional and time-of-use rates were used to shape the degree of DR. Median annual peak demand savings with window and thermostat control across all climate zones were 24.3 kW (4.4 W/m2) per building or 15.9 W/m2 for non-north perimeter zones. Resource adequacy at the whole building level was estimated to be 13.1 to 43.4 $/kW per year over the 30-year life of the installation. Co-benefits were increased energy efficiency, and reduced electricity cost and emissions. Visual and thermal comfort requirements were met at all times. Dynamic facades controlled by MPC have substantial technical potential for DR across all U.S. climates and warrant serious consideration for inclusion in DR portfolios.

Energy Waste Reduction in University of Ibadan, Nigeria’s Water Factory Using Energy Audit Approach

The technical cost incurred in providing adequate services for drinkable water production and distribution is high in developing countries of which Nigeria is no exception. By analysing the energy aspect of this cost, with a view of eliminating non-value-adding energy wastes, the cost of production can be reduced. In this work, an energy audit of the University of Ibadan water factory has been carried out, as a means of reducing the electrical energy wastes within the factory, as well as the associated costs to these energy wastes. Data on equipment being used and their power ratings were collected via walkthrough audit, by means of interviews and personnel observation, and thereafter inputted into Excel spreadsheet in order to determine the extent of energy consumption of each of the factory equipment, areas of energy wastage and areas of energy conservation opportunities. The factory’s structural data was obtained through the use of a 50m fibre measuring tape, in order to carry out lighting, air-conditioning and ventilation assessments on selected rooms in the factory, as means of proper energy conservation. Energy management measures were deployed by suggestion of more efficient equipment. Major equipment found includes automatic filling machine, pumps, steam generator, conveyor etc. The total electrical energy consumption of the factory was 9,280.899 kWh, of which equipment such as pumps took 20% (highest energy consumption). Areas of major energy consumption include pet blowing room, production room 1, production room 2 and the borehole area. While the current cooling capacities of most rooms in the factory were above the cooling requirement of each room, the luminosity requirement of selected rooms in the factory is surpassed by the current luminosity level found. Energy management measures were found to reduce the energy consumption of the factory by 17%, saving a sum of 87,906.207 naira of electrical energy cost monthly. In conclusion, the energy audit carried out on the University of Ibadan water factory was found useful in reducing energy waste and associated costs within the water factory. This approach can be applied in industries for electricity cost reduction, hence, production cost.

Integrating Vehicle-to-Home Unit with Smart Home Energy Management Systems: A Unified Energy Management Framework

The increasing acceptance of electric vehicles (EVs) and renewable energy sources (RES) has led to the need for a unified energy management framework that can integrate multiple energy systems, including Vehicle-to-Home (V2H) units, Smart Home Energy Management Systems (SHEMS), Solar PV systems, and the grid. The framework uses a centralized control strategy that makes decisions based on real-time integration of a Vehicle-to-Home (V2H) unit with a Smart Home Energy Management System (SHEMS), which can provide a solution to these challenges by enabling bidirectional power flow between the EV battery and the home. We proposed a unified energy management framework that integrates a Ve- hicle-to-Home (V2H) unit with a SHEMS using a Perturb and Observe (P-&-O) algorithm. The proposed framework allows for the optimization of energy usage in the home by utilizing the battery of an EV, which can be charged during off-peak hours and discharged during peak hours. The P&O algorithm is a well-known algorithm used in photovoltaic systems to track the Maximum Power Point (MPP) of a solar panel. The algorithm is also applicable to EV charging and discharging systems. The algorithm uses a per- turbation signal to change the charging rate of the EV battery and observes the resulting change in the bat- tery voltage. The simulation results can also provide benefits to EV owners, such as reduced electricity costs and increased flexibility in managing their EV charging, helping to achieve efficient and sustainable energy management in residential areas. Keywords: Solar panel, Electric Vehicle Battery, Vehicle-to-Home (V2H), Smart Home Energy Manage- ment Systems (SHEMS), Demand side management.

Optimization of electricity consumption in an industrial zone with a battery system: a case study of Ikitelli industrial zone in Turkey

Industrial power use is considerably high in Turkey. In the case of industrial zones, the three-time tariff is more economical for the owners. However, in industrial zones with considerably high energy consumption during peak hours, an alternative way is needed to reduce electricity costs. A battery-powered model was developed earlier in order to take advantage of the three-time tariff and to reduce the electric charge during peak hours. In this study, the aim was to reduce the power utility costs of industrial consumption and to mitigate CO2 emissions for businesses with high electricity costs by using solar energy. A mixed-integer optimization model was constructed as a combination of solar panels and a battery system to minimize the peak-hour grid-connected energy consumption in industrial zones. Using the consumption data from 2018, a model was implemented for a case study in İkitelli Industrial Zone in terms of 24-h scheduling. Three different alternative energy combination scenarios were studied: a monofacial PV (photovoltaic) panel system with batteries, a bifacial PV panel system with batteries, and electricity received from the grid only at the night tariff. This study found that a combined method could minimize the grid usage during peak hours.

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.

Technical and economic analysis of a pebble bed modular reactor with nitrogen coolant

High-temperature nuclear reactors (HTR) could benefit from using the gas turbine in tandem with a steam turbine; this is the principle of combined cycles using natural gas. HTRs have the characteristic that the turbine works in high-temperature conditions. The flue gas could feed a steam generator to produce additional electricity improving system efficiency. The current study assesses the likeliness of having a combined cycle from a Pebble Bed Modular Reactor using nitrogen as a coolant. The balance of the plant and the corresponding configuration to meet this goal are given; the results show an efficiency of 53.56% of electrical generation compared to 42% reported by the HTR-PM. The share cost for turbine plant equipment represents 8.9%–14.0% of the total cost of the power plant. Three scenarios are contemplated to assess the economic impact of the additional generation where an increase in the overnight cost is considered, 9.99%, 12.41%, and 15.73% because of the additional cost of the steam turbine and associated equipment. Assuming a 4500 US$/kW cost for installed capacity for the HTR-PM reactor, for a 10% discount rate, the electricity total levelized cost is 95.56 US$/MWh. For the proposed combined cycle using nitrogen as a coolant, the electricity total levelized cost is 89.78 US$/MWh, assuming a 10% discount rate when the higher overnight cost is taken into account (5208 US$/kW). Using nitrogen under this alternative will produce an additional electricity production of 425,742 MWh annually. It leads to an electricity cost reduction of 5.78 US$/MWh even in the higher cost for the nitrogen combined cycle here considered, and a 10% discount rate compared with the HTR-PM steam cycle reported data, showing the technical and economic feasibility of using nitrogen as a coolant gas.

Efektivitas pembangkit listrik tenaga mikrohidro sebagai penyedia energi baru terbarukan berbasis komunitas

Indonesia is currently trying to achieve the number 7 sustainable development target in the energy sector through renewable energy. This is evidenced by achieving an electrification ratio of 99.20% in Indonesia, with one of Indonesia's renewable energy potentials in micro hydro to provide electricity in rural areas. This research will discuss the potential for providing micro-hydro power plants (PLTMH) outside Java, especially in West Papua Province. The research was conducted qualitatively, using a comparative study research method to compare research variables, namely the operational efficiency of the Anggi PLTMH in Anggi District, Arfak Mountains, West Papua, and the Kali Ombak PLTMH in South Mare District, Maybrat Regency, West Papua from an environmental aspect (analysis of electricity production from the PLTMH, carbon dioxide emissions resulting from the operation of the PLTMH, and a comparison of environmental impacts based on the type of environmental documents) and social aspects (reduction in the cost of community electricity due to the implementation of the PLTMH, the number of households channeled electricity, and the implementation of community involvement in the management of the PLTMH). The study results show that the Anggi PLTMH is more effective than the Kali Ombak PLTMH in providing micro-hydropower in terms of electricity production capacity, reduction of carbon dioxide emissions, and reduction of electricity costs. PLTMH Anggi can generate electricity of 108.9 kW, reduce carbon dioxide by 2.1693 tons of CO2, and save electricity costs of Rp 4,441,891.608, which is superior to PLTMH Kali Ombak. However, currently, there is still a need for development in the social field in the operation of PLTMH, both PLTMH Anggi and PLTMH Kali Ombak, in the form of community involvement because active community involvement has not yet been realized, both in the planning and maintenance stages of PLTMH and the socialization of environmental impacts based on the type of environmental documents to be able to manage the impact perspective. The community's negative attitude toward PLTMH activities.

COSTS REDUCTION OF MAIN FANS OPERATION ACCORDING TO SAFETY VENTILATION IN MINES – A CASE STUDY

Considering the various hazards present in an underground mine, safe ventilation entails maintaining a certain airflow rate in the mining workings. The air velocity, airflow rate and air composition must be regulated in a ventilation network by operation of the main fans. On the other hand, ventilation costs must be minimized. Thus, the optimal solution is when the requisite safety features are fulfilled and the power output of the fans is at a minimal yet sufficient rate. In real-world mines, it is common to have airstreams interlinking subnets of the main fans. The current restructuring of the mining sector commonly involves the connection of different mines, which leads to an increase in the number of such cases. This article presents the results of research into ventilation networks containing these kind of airstreams and introduces a new method for reducing ventilation costs. The method is based on an algorithm which allows the determination of the resistance of a stopping, the head of the fans and the air quantity for which air distribution is optimal. As a result, the total power output of the fans is at the lowest level that yields a reduction in ventilation costs. The method was applied for two theoretical examples and a practical one which was taken from a real ventilation network. In the first example, fan power output was reduced by 17.252 kW, which gave an annual reduction of 188.909 MWh, and an annual electricity cost reduction of €27014. Therefore, optimization enabled a saving of approximately 14% of the costs. In the second example, power output was reduced by 106.152 kW, which produced an annual reduction of 1162.364 MWh, and an annual electricity cost reduction of €166218. In this case, optimization allowed a reduction in costs of approximately 40%. Considering both examples, the cost reductions did not affect the safe airflow rate. For the real-world mine, the annual savings were 2343 MWh, which corresponds to approximately €335000.

A new biomass-based hybrid energy system integrated with a flue gas condensation process and energy storage option: An effort to mitigate environmental hazards

The development of biomass-fueled cogeneration plants can be fruitful to achieve the sustainable development goals, due to the crises of fossil energy-fueled ones. Additionally, biomass fuels can offer higher reliability and more sustainable energy compared to the two more well-known renewables (i.e., solar and wind energies) due to the no dependence on environmental and weather conditions. The present article provides a comprehensive assessment (thermodynamic-design, exergoeconomic and environmental) and optimization of a new biomass-driven cogeneration plant cascaded with a heat recovery unit. The planned plant is based on a biomass gasification process, some thermodynamic power generation cycles based on steam and gas turbines (i.e., Rankine cycle, Brayton cycle, and organic Rankine cycle (ORC)), and flue gas condensation process. Besides, a compressed air energy storage system (CAESS) as an energy storage process is integrated with the considered plant to establish a balance between production and demand and reduce electricity costs. To achieve optimal performance, a multi-objective optimization (based on a genetic algorithm) is applied. The findings showed that by considering the multi-objective optimization techniques, the plant's exergy efficiency could improve by about 15%. At the same time, the exergy cost rate is reduced by almost 17.3%. Avoiding the payment of carbon tax and the high electricity production level in the on-peak consumption period are unique and competitive features of the proposed biomass-driven power plant. It was also found that the LCOE and the payback period were 13.27 USD/GJ and 2.03 years, respectively, therefore, the proposed power plant exhibits reasonable economic feasibility.

Realistic Home Energy Management System Considering the Life Cycle of Photovoltaic and Energy Storage Systems

Home Energy Management Systems (HEMSs) have become necessary due to energy security and climate change concerns. Scheduling the operating time of household appliances is one of the most effective strategies used by HEMSs to reduce electricity costs, with several studies proposing optimization strategies for scheduling home appliances to reduce the grid energy usage cost. This work considers energy usage costs from Renewable Energy Sources (RESs) and Energy Storage Systems (ESSs) in the appliance-scheduling strategy and energy flow management. The objectives are reducing the real electricity cost while maintaining a longer battery lifespan, reducing battery charging/discharging losses, and using PV power efficiently. To achieve this, we developed a pricing model of battery energy usage, in addition to modeling the PV energy usage cost based on the Levelized Cost of Energy (LCOE) for PV systems. PV-battery energy usage cost models were introduced into the optimization problem solved using the Augmented Grey Wolf Optimization (AGWO) and Particle Swarm Optimization (PSO) algorithms in MATLAB. We developed an efficient energy flow management algorithm. We collected real data from a home in Vigo, Spain, and simulated four scenarios. The results show that the proposed system using AGWO and PSO reduced the real cost by 25.87% and 25.98%, respectively. Compared with an existing energy-usage-pricing model, the AGWO reduced the energy losses by 40.429% and extended the battery lifespan by 68.282%. Similarly, the PSO reduced the energy losses by 45.540% and extended the battery lifespan by 84.56%. Moreover, the proposed system reached the breakeven point of the system in a shorter time.

Multi-objective electricity cost and indirect CO2 emissions minimization in commercial and industrial buildings utilizing stand-alone battery energy storage systems

A large portion of global carbon emissions are attributable to electricity generation. Several previous studies indicate that both electricity cost and carbon emission reductions are not attainable with stand-alone battery energy storage systems for residential buildings. However, in this study, lithium-ion battery energy storage dispatch (charging and discharging) is optimized as a multi-objective decarbonization and cost-saving strategy in ten commercial and industrial facilities. The analysis tests 100 energy storage capacities, 5 discharge times, and 2 control strategies with and without enrollment in event-based demand response. Unlike smaller energy consumers, the results show significant indirect CO2 emissions reductions (>31%) paired with significant electricity cost reductions (>10%) are possible from stand-alone battery energy storage systems in a large commercial facility. Additionally, the results indicate that enrollment in the event-based demand response program and dispatch under the load shifting control strategy are always optimal to minimize both the discounted payback period and the indirect CO2 emissions.

Application of Demand-Side Management (DSM) for evaluation and optimization of Electric Vehicle’s charging cycles

Electric vehicles (EVs) have been known as potential transportation alternatives for internal combustion engines thanks to lower carbon emissions and high energy efficiency. The combination of EVs with other renewable energy sources (RES) is a viable solution for eliminating the emission on the road and during the electricity generation process, reducing the reliance of EVs energy demand on the non-renewables. However, the higher penetration of EVs and unpredictable charging dynamics could burden the power system tremendously. This study proposes a demand-side management (DSM) strategy for an EVs-integrated residential grid with the assistance of grid-tied rooftop photovoltaic (PV) systems. The main objectives are to minimize the peak and fluctuation load while ensuring the EVs’ effective charging and vehicle-to-grid (V2G) scheme based on time-of-use (TOU) tariff and real-time pricing (RTP). The EVs’ traveling distances, driving periods, initial state-of-charge (SOCs), and plugging states are considered. One-day simulation in different scenarios was carried out to evaluate the strategies. The results indicate that the proposed model can reduce load demand, flatten the load curve, and reduce electricity costs, maintaining the system’s stability. The approach can be an indicator for load aggregators and utilities to manage PEVs as DSM tools in the system.

РОЗРОБКА АВТОНОМНОГО ЕНЕРГОЗАБЕЗПЕЧЕННЯ ПЕРЕСУВНОЇ ПОВЕРХНЕВОЇ ДЕГАЗАЦІЙНОЇ УСТАНОВКИ

Currently, in connection with the difficult situation with energy supply, issues related to the application of new measures to conserve energy resources as well as the introduction of new promising technologies using alternative energy sources, such as mined methane, have gained great importance. The problem of methane from coal deposits is of particular importance. This problem primarily concerns the safety of miners' work, methane production as an alternative source of energy resources, and solving environmental problems related to methane emissions into the atmosphere of coal mining enterprises. The problem of reducing the efficiency of degassing is caused by the long length of the degassing pond and the depth of cleaning operations. The use of a mobile surface degassing unit makes it possible to effectively carry out degassing and achieve significant volumes of extracted gas with a methane content of up to 80%, but high costs of electricity complicate its operation. Therefore, the paper proposes an autonomous power supply for a mobile surface degassing plant. For the autonomous power supply of a mobile surface degassing plant, it is advisable to use an electric generator since an uninterrupted supply of electricity is important. Gas power plants are widely used in industrial enterprises. In accordance with the power consumption of the electrical equipment of the mobile surface degassing plant, the work justified and selected a gas power plant and electric power equipment for the autonomous power supply of the mobile surface degassing plant. The gas-piston power plant is designed for the production of electrical energy and can be both the main and backup source. As a fuel, mainline natural gas with an average supply pressure or methane mine gas is used. The basic configuration of the power plant is a liquid-cooled gas-piston engine based on a diesel unit. A technological scheme for connecting the gas power plant to a mobile vacuum pumping unit and its power supply scheme has been developed, which will increase the safety of cleaning works, solve the issue of methane utilization, significantly reduce electricity costs, and improve the environmental situation in the industrial region.

Systematic effects of flexible power-to-X operation in a renewable energy system - A case study from Japan

For achieving a carbon–neutral energy system, economical flexibility mechanisms are crucial to accommodate intermittent and decentralized renewable energy sources. Power-to-X (P2X) is a promising technology for this purpose. This study aims to elucidate the systematic effects and competition of dynamically operated power-to-X (P2X) technologies as a flexibility option in comprehensive renewable energy systems. We developed a linear programming model to optimize energy systems incorporating P2X technologies, including water electrolysis, methanation, Fischer–Tropsch synthesis, and Haber–Bosch synthesis, and investigated the impact of P2X flexibility on the system structure and energy costs, focusing on Japan as a case study. The results demonstrate that each P2X technology effectively shifts electricity loads and reduces curtailment by more than 80%. The contribution of each P2X technology varies, with water electrolysis playing a dominant role due to its relatively low fixed costs and large scale. Furthermore, the mechanism significantly reduces system costs by 35% and supply costs of electricity and hydrogen by 41% and 30%, respectively, by reducing the required capacities of electricity generators, stationary batteries, and transmission grids. Therefore, P2X has a cost advantage over these flexibility options. The results also highlight that the maximum effect is achieved when the capacity factor of P2X is 30%. However, synthetic hydrocarbons would require a carbon price of over 356 EUR/tCO2 to compete with fossil fuels. Domestic electrofuels would face tough competition with global fuel costs. Nevertheless, the reduction in electricity costs through P2X, along with its contribution to energy security, may incentivize its adoption.

Administration strategy of energy management in smart grid: system view and optimization path

Power generation and transmission infrastructure is vulnerable to the interaction of various Distributed Generations (DG), which leads to the imbalance of power system operation, frequent voltage drops or spikes, and even power outages. This phenomenon not only wastes energy, but also affects grid security. The main reason is a delayed feedback of circuit failure and load changes, and the optimization of energy management system and path is an effective way to solve the above problems. In this paper, a method of multi-objective optimization based on ANFIS algorithm is proposed which can help to improve the demand response, energy storage and management of smart power grid, reduce the volatility of DGs, reducing electricity costs and improving energy efficiency. Firstly, based on the ANFIS algorithm, the distributed power generation control mode, inverter control, real-time electricity price calculation method, energy transfer and storage scheme are improved, and the optimization path of the energy management system is defined. Secondly, the advantages of ANFIS algorithm in response speed and running stability are verified by comparing with other algorithms. Finally, a distributed energy microgrid is constructed for simulation verification. The results show that :(1) ANFIS optimization algorithm has good adaptability in smart grid, and has advantages in large amount of data processing and information transmission; (2) The verification model based on ANFIS has strong elasticity and efficient response speed. The research results will help solve various problems in the smart grid, including establishing a clear energy management system path, maintaining the stable operation of the power system, providing users with more reasonable power plans and the lowest cost of electricity.

Optimized Power Dispatch for Smart Building and Electric Vehicles with V2V, V2B and V2G Operations

The operation of smart buildings (with solar, storage and suitable power routing infrastructure) can be optimized with the addition of parking stations for electric vehicles (EVs) with vehicle-to-everything (V2X) operations including vehicle-to-vehicle (V2V), vehicle-to-building (V2B) and vehicle-to-grid (V2G) operations. In this paper, a multi-objective optimization framework is proposed for the smart charging and discharging of EVs along with the maximization of revenue and savings of smart building (prosumers with solar power, a battery storage system and a parking station) and non-primary/ordinary buildings (consumers of electricity without solar power, a battery storage system and parking station). A mixed-integer linear program is developed to maximize the profits of smart buildings that have bilateral contracts with non-primary buildings. The optimized charging and discharging (V2X) of EVs at affordable rates utilizing solar power and a battery storage system in the smart building helps to manage the EV load during on-peak hours and prevent utility congestion. The results indicate that in addition to the 4–9% daily electricity cost reductions for non-primary buildings, a smart building can achieve up to 60% of the daily profits. Further, EVs can save 50–69% in charging costs while performing V2X operations.