Model Centric Development of Genetic Algorithm Based Optimal Load Scheduler for Smart Home

A high peak demand causes the escalating cost of electricity costs for both the utility and end-users. This paper investigates the challenges raised by the high peak demand and the state-of-the-art technologies adopted to reduce the peak demand. The peak shaving technologies can be categorized into four groups. The first category is peaking power plants which include the open-cycle gas turbine (OCGT) power plants and hydroelectric power plants. The second category is the energy storage system (ESS) comprising of the pump hydro storage (PHS), compressed air energy storage (CAES), battery energy storage (BES), flywheel energy storage, thermal energy storage (TES) and hydrogen fuel cell. The third category is the hybrid renewable energy – storage system (HREES) that combines the renewable energy (RE) and energy storage system. The fourth category is the demand-side management (DSM. The advantages and shortfalls of these technologies are highlighted in this paper.

Multi-stall fast charging stations are often thought to require megawatt-range grid connections. The power consumption profile of such stations results in high cost penalties due to monthly power peaks and expensive linkage fees. A local energy storage system (ESS) can be used to address peak power demands. However, no appropriate sizing method is available to match specific constraints, such as the contracted power available from the grid and the projected recharging demand. A stochastic distribution of charging events was used in this paper to model power demand profiles at the station, with a one minute resolution. Based on 100 simulated months, we propose an optimum number of charging points, and we developed an algorithm to return the required local ESS capacity as a function of the available grid connection. The role of ESSs in the range of 100 kWh to 1 MWh was studied for all stations with up to 2000 charging events per week. The relevance of ESS implementation was assessed along three parameters: the number of charging points, the available grid connection, and the ESS capacity. This work opens new possibilities for multi-stall charging station deployment in locations with limited access to the medium voltage grid, and provides sizing guidelines for effective ESSs implementation. In addition, it helps build business cases for charging station operators in regions with high demand charges.

Assessing the impact of local energy generation and storage to achieve the decarbonization of the single-family housing stock in Germany

The variability of large scale wind power generation at high penetration levels has a significant impact on the secure and economic operation of onshore power systems. Grid bottlenecks, high gradients and wasting of wind energy can be avoided using local Energy Storage Systems (ESS) with dedicated power and capacity. The storage type considered in this study is an adiabatic compressed air energy storage (ACAES). This paper presents a comparison of different ESS applications aiming both to increase the profit of the power plant operator in selling the electricity into the electricity market and to support the intermittent electricity production of the wind park. This contribution presents the investigation methodology and discusses computation results for a region in Germany.

Transactive energy is a two-way exchange of energy between the electric power grid and a community’s distributed energy resources, offering opportunities for efficiency improvements through market-based economic and control techniques. A community’s distributed energy resources include electricity-producing resources and controllable loads. Increased usage of unsynchronized generation of non-dispatchable solar photovoltaic energy and household demand at the community level can adversely affect the power quality, reliability and network balancing of the electricity grid. A solution was developed in this paper in the form of energy storage and demand side management on a solar residential community. An agent-based transactive energy management system was developed and simulated using multiple prosumer houses with roof-top PV systems and local energy storage. Experimental work conducted on an archetype house, near Toronto, Ontario, Canada, was used to model an all-electric residential house and clusters were created with varying orientations and building properties to mimic different efficiency levels of the houses within the virtual community. A machine learning algorithm using historical data and weather forecasts from Natural Resources Canada (NRCan) was used to forecast the community’s energy generation as well as building’s thermal loads. In this community, consumers can curtail their loads based on price signals sent to smart devices in homes. Open-loop mixed integer linear programming technique (MILP) and model predicted control (MPC) were compared and evaluated. The simulation shows promising results with a 9% energy savings during the summer solstice day and 5% during the winter solstice day when compared to the normal operation of houses’ mechanical equipment.

Asymmetric supercapacitor comprising a core-shell TiNb2O7@MoS2/C anode and a high voltage ionogel electrolyte

A microgrid (MG) is a discrete energy system consisting of an interconnection of distributed energy sources and loads capable of operating in parallel with or independently from the main power grid. The microgrid concept integrated with renewable energy generation and energy storage systems has gained significant interest recently, triggered by increasing demand for clean, efficient, secure, reliable and sustainable heat and electricity. However, the concept of efficient integration of energy storage systems faces many challenges (e.g., charging, discharging, safety, size, cost, reliability and overall management). Additionally, proper implementation and justification of these technologies in MGs cannot be done without energy management systems, which control various aspects of power management and operation of energy storage systems in microgrids. This review discusses different energy storage technologies that can have high penetration and integration in microgrids. Moreover, their working operations and characteristics are discussed. An overview of the controls of energy management systems for microgrids with distributed energy storage systems is also included in the scope of this review.

Positive energy districts and sustainable plus energy neighbourhoods are developed in the European context to reduce energy use and greenhouse gas emissions from the building sector. The planning and development of positive energy districts and sustainable plus energy neighbourhoods is complex and requires collaboration between stakeholders and new measures to achieve high energy efficiency, local renewable energy generation, energy storage and flexibility, and energy sufficiency. This paper examines the implementation of energy measures in the planning and design of four positive energy district and neighbourhood development projects in Norway and Sweden. The paper compares the two different institutional and energy system contexts and how these affect the development of positive energy districts, focusing on the perspectives of the municipality and developers. Existing academic literature and positive energy district guidelines are used to develop an analytical framework for the planning and design of positive energy districts and sustainable plus energy neighbourhoods. Results highlight an early focus on energy ambitions, wide stakeholder involvement, and the importance of aligning interests between stakeholders and working interdisciplinary in the planning and design phases to find optimal energy measures. Both the building and the neighbourhood/district level are important to increase energy efficiency, energy sufficiency, and energy flexibility, and consequently lower the environmental impact of the whole development project.

Optimal planning and configuration of adiabatic-compressed air energy storage for urban buildings application: Techno-economic and environmental assessment

Smart grid solutions with enabling technologies such as energy management systems (EMS) and smart meters promote the vision of smart households, which also allows for active demand side in the residential sector. These technologies enable the control of residential consumption, local small-scale generation and energy storage systems to respond to time-varying prices. However, shifting loads simultaneously to lower price periods is likely to put extra stress on distribution system assets such as distribution transformers. Especially, additional new types of loads/appliances such as electric vehicles (EVs) can introduce even more burden on the operation of these assets, which is an issue that needs special attention. Such extra stress can cause accelerated ageing of distribution system assets and significantly affect the reliability of the system. In this study, the impact of a smart neighborhood load on distribution transformer ageing is investigated. The EMS of each household is designed to respond to prices and other signals emitted by the responsive load serving entity within the relevant demand response (DR) strategy. An optimization framework based on mixed-integer linear programming (MILP) is presented in order to define the EMS structure. Then, the equivalent ageing of the distribution transformer is examined with a thermal model under different scenarios. The case studies that are presented indicate that the integration of EVs in residential premises may indeed cause accelerated ageing of the distribution transformers, while the need to investigate the efficiency of dynamic pricing mechanisms is rendered evident.

Connection of renewables to the distribution system is growing very rapidly. The inherent uncertainty of renewable energy sources, however, can potentially introduce operational conflicts, disrupting the typical operation pattern of local voltage regulation devices and causing system voltage rise beyond the acceptable limits. This paper describes a joint platform to maintain voltage regulation in a distribution network incorporating a large share of PV energy sources. With an objective to improve the voltage profile and minimize feeder losses, the operation of local voltage controllers and energy storage is coordinated. An optimization approach, based on the genetic algorithm, is developed to find out the optimal settings of the load tap-changing transformers and switched shunt capacitors, as well as the optimal dispatch of energy storage. The proposed method is applied to the IEEE 123 test feeder, using time series analysis over a daily, 24 h, simulation period. Test results clearly exhibit that the close coordination between the voltage control devices and energy storage scales back system energy losses, minimizes the interaction among local voltage controllers and prevents reverse power flow. It consequently upholds the voltage at the customer terminals within the statutory limits, under high penetration levels of solar-fueled generation.

In this paper, agent-aggregators cooperation has been introduced in Smart Microgrid (SMG) consisting of household users, Photo Voltaic array (PV), and Local Energy Storage System (LESS). The SMG has Energy Management System called agent deal with two aggregators to purchase the energy for the consumers. Each aggregator supplying a limited level of energy from different energy resource, where one aggregator depend on Community Energy storage System to provide power in one direction to the users and the other aggregator supplying power from the utility grid. The job of the agent is to decide the optimum amount of purchasing power from the aggregators at the lowest cost to supply the users and to charge the LESS taking into account the constraints of supplying limitations for both CESS and utility grid at Point of Common Coupling. Each aggregator supplies a limited level of energy from different energy resource, where one aggregator depend on the Community Energy storage System (CESS) to provide power in one direction to the users and the other aggregator supplying power from the utility grid. The job of the agent is to decide the optimum amount of purchasing power from the aggregators at the lowest cost to provide the users and to charge the LESS taking into account the constraints of providing limitations for both CESS and utility grid at Point of Common Coupling (PCC).A model-free Reinforcement Learning (RL) method is used as an essential strategy to achieve the proposed task using Deep Q-Network (DQN). The simulation result is done and the results are verified using Improved Particular Swarm (IPSO) as a benchmark method. The obtained results show the efficiency and effectiveness of the method used

Fault-ride-through strategies for grid-tied and grid-forming smart-transformers suited for islanding and interconnected operation

SUMMARYIn the future, renewable energy (RE) generation systems will be constructed and interconnected to the power system by nonutility entities such as generation companies and residential customers. Therefore, generation expansion planning (GEP) should consider the effects of RE generation. The purpose of this paper is to discuss the best GEP from the economic, supply reliability, and environmental aspects considering the penetration of RE generation. For this purpose, this paper proposes a model of a utility's GEP process considering RE generation. The proposed method is based on stochastic dynamic programming (SDP) in which long‐term uncertainties are modeled by geometric Brownian motion (GBM) and the binomial lattice process. The variation of RE generation output due to the weather conditions (short‐term uncertainty) is also considered in the proposed method by means of the net load curve.

Solar energy has become one of the most significant renewable energy options, Because of the decline in fossil fuels. Conversion of Solar energy into Electric energy has been harvested through DC-DC Converters and stored in Batteries. A "Smart Multiport Bidirectional non isolated DC-DC Converter for PV-Battery Systems" has been proposed in this paper. One port is for unidirectional traffic, while the other is for bidirectional traffic. Previously, a SISO (single input single output) power flow system used, which resulted in power quality problems that were beyond the PV converters settings. In order to resolve the power quality problem and provide continuous supply, it has been changed into a multiport device with the use of LES (Local energy storage). High reliability, high power density, and fewer conversion stages are the advantages of this modified technique. A simulation work performed for all modes of proposed converter for low voltage levels. Simulation result ensures the satisfactory performance of new converter. IOT is introduced in this proposed converter to maintain a continuous power flow. So that the entire system can be monitored and regulated. IOT is an external module that can be added or removed based on specifications that vary by venue, context, and other factors. A new model has been implemented using 24V solar PV panel, 12V battery and the bidirectional power flow achieved when solar energy fails to charge LES.