From Connected HVAC to Climate Intelligence System: A Reference Architecture for Next-Generation Smart Homes

The Bi-level Multi-Objective Planning Model of Solar PV-Battery Storage-Based DERS in Smart Grid Distribution System is a research paper that proposes a planning model for the implementation of distributed energy resources (DERs) in a smart grid distribution system. The model is designed to optimize the deployment of solar PV and battery storage systems in the grid, while taking into account various technical, economic, and environmental factors. The proposed planning model is based on a bi-level multi-objective optimization approach, which considers both the objectives of the utility and the objectives of the DER owners. The upper-level objective is to minimize the total cost of energy supply to the grid, while the lower-level objective is to maximize the revenue of the DER owners. The model is implemented using a genetic algorithm, which is used to search for the optimal solution.
 The model is also capable of considering the uncertainties associated with solar PV and battery storage systems, such as weather conditions and battery degradation. The results of the study show that the proposed planning model can effectively optimize the deployment of solar PV and battery storage systems in a smart grid distribution system. The model is also shown to be robust to various uncertainties associated with DERs, such as weather conditions and battery degradation. Overall, the proposed planning model provides a valuable tool for the implementation of distributed energy resources in a smart grid distribution system. By optimizing the deployment of DERs, the model can help to reduce the cost of energy supply, while also improving the reliability and environmental performance of the grid.
 This project describes the multi objective battery sizing and storage system for grid connected system using renewable energy solar and wind system.

Modelling and simulation are important tools that have novel applications in all engineering disciplines because they combine engineering and technology. Large systems that involve different domains can be modelled and integrated. This approach facilitates modelling and simulation of complex real-world systems. As residential heating systems become electrified, their contribution to the residential load becomes significant. In this paper we investigate multi-physical domain simulation model for residential space heating. An electric heat pump heating system for a detached house is simulated and analysed with thermal and electric domain models. Analyses are performed on the operational cycles of the heat pump based on occupants' thermal comfort preferences. The potential effects of aggregated number of residential heat pumps on the electricity network are briefly studied

With an increase in the number of electric vehicles (EVs) and battery storage systems (BSSs), there are major challenges in the distribution grid to maintain a scaling control structure. Also, with the vehicle-to-grid (V2G) technology, EVs can now inject power into the grid for voltage regulation. Here, this article proposes a new distributed real-time alternating direction method of multipliers (ADMM) technique to control EVs and BSSs for voltage regulation while maximizing their utility function. More specifically, a continuous-domain real-time optimization and control algorithm is developed in closed form, which exchanges relevant information among the neighboring nodes through the communication network and optimizes a combined convex objective of EVs and BSSs welfare and voltage regulation with power flow equations as constraints. Convergence analysis is provided using the Lyapunov direct approach, and simulation results are included to illustrate the effectiveness of the proposed scheme. Note to Practitioners —This article is motivated by the problems that an electric distribution system is facing today due to the penetration of electric vehicles and battery storage systems (BSSs). The presence of a large number of electric vehicles (EVs) and BSSs is causing a substantial degradation of the quality and reliability of the power grid. Despite the adverse effects, EVs and BSSs can be controlled and used as a power source to benefit the grid. Also, this control of EVs and BSS cannot be done by a centralized body since this would be nonscalable and would require huge communication bandwidth. To tackle this ever scaling problem, this article develops a continuous-domain multiagent distributed algorithm to control the EVs and BSSs and utilize them to maintain the grid voltage within the normal operating range while also satisfying the consumers by maximizing their welfare. The algorithm was developed in the continuous domain since, in most of the other algorithms that are iterative and in discrete time, the accuracy of the optimal solution greatly depends on the sampling time, and thus, it is not robust to changes that are prevalent with distributed energy resources such as EVs and BSSs.

This paper aims presenting a methodology for economic dispatch (ED) of island grids with distributed energy resources (DERs). The method utilizes an algorithm dedicated to battery storage systems (BSSs) placement optimization in a given power system using genetic algorithms (GA), where daily time varying loads, wind power generations, and diesel power generator operation scheduling are considered together with BSSs characteristics, including capacity, installation location, and charging/discharging. The problem is formulated as a non-differential combinational optimization problem to solve the ED of the BSSs and power units, where the total system cost to be minimized is subject to capacity and system operation constraints. A practical island power system is selected for computer simulations to ensure and demonstrate the performance of proposed method and explore the benefits of BSSs to system operations.

Power systems worldwide are experiencing rapid evolvements with a massive increase of renewable generation in order to meet the ambitious decarbonization targets. A significant amount of renewable generation is from Distributed Energy Resources (DERs), upon which the system operators often have limited visibility. This can bring significant challenges as the increasing DERs’ can lead to network constraints being violated, presenting critical risks for network security. Enhancing the visibility of DERs can be achieved via the provision of communication links, but this can be costly, particularly for real time applications. Digital Twin (DT) is an emerging technology that is considered as a promising solution for enhancing the visibility of a physical system, where only a limited set of data is required to be transmitted with the rest data of interest can be estimated via the DT. The development and demonstration of DTs requires realistic testing and validation enviorment in order to accelerate its adoption in the industry. This paper presents a real time simulation and hardware-in-the-loop (HiL) testing platform, specifically designed for prototyping, demonstrating and testing DTs of DERs. Within the proposed platform, a software-based communication emulator is developed, which allows the investigation of the impact of communication latency and jitter on the performance of DTs of the DERs. Case studies are presented to demonstrate the application of the developed DT prototyping process and testing platform to enable frequency control using the DTs, which provide valuable learnings and tools for enabling future DTs-based solutions.

This chapter is written with the intent to explore the history, architecture, applications, and challenges in the implementation of digital twin with IoT competences. Digital twins are considered to be a fundamental starting point for today's smart city construction. The chapter initiates with a brief description of the concepts of digital twins and digital twin for cities and smart homes, discusses the relationship between digital twins and smart cities, analyses the characteristics of smart cities and homes based on digital twins, and focuses on the main applications of smart cities based on digital twins. This chapter sheds light on the future development of smart cities and smart homes based on digital twins.

The last decade the continuous integration of Distributed Energy Resources (DER) along distribution networks, follows an uncoordinated fashion posing manifold technical challenges for the operation of the grid. However, DER could be actively incorporated in the operation of distribution networks providing certain flexibility. Such DER flexibility is generally associated with temporal shifting of energy (i.e. for consumption or injection). This work assesses the active management of multiple DER in a coordinated manner in Low Voltage (LV) distribution networks. The flexible use of DER is hereby regarded for the provision of support to the LV grid, mainly for voltage regulation and phase balancing, line congestions management as well as to ensure rated power for the transformer of the secondary substation. A control framework is proposed for the management of DER flexibilities, which relies on a three phase multi-period optimal power flow. A study based on a real -IEEE benchmark- LV distribution network is presented to demonstrate and quantify the importance of active management of DER such as battery storage system, electric vehicles (with vehicle to grid operation) and microgeneration.

Battery storage devices have been widely utilized for different applications. However, for high power applications, battery storage systems come with several challenges, such as the thermal issue, low power density, low life span and high cost. Compared with batteries, supercapacitors have a lower energy density but their power density is very high, and they offer higher cyclic life and efficiency even during fast charge and discharge processes. In this dissertation, new techniques for the control and energy management of the hybrid battery-supercapacitor storage system are developed to improve the performance of the system in terms of efficiency, power quality and reliability. To evaluate the findings of this dissertation, a laboratory-scale DC microgrid system is designed and implemented. The developed microgrid utilizes a hybrid lead-acid battery and supercapacitor energy storage system and is loaded under various grid conditions. The developed microgrid has also real-time monitoring, control and energy management capabilities. A new control scheme and real-time energy management algorithm for an actively controlled hybrid DC microgrid is developed to reduce the adverse impacts of pulsed power loads. The developed control scheme is an adaptive current-voltage controller that is based on the moving average measurement technique and an adaptive proportional compensator. Unlike conventional energy control methods, the developed controller has the advantages of controlling both current and voltage of the system. This development is experimentally tested and verified. The results show significant improvements achieved in terms of enhancing the system efficiency, reducing the AC grid voltage drop and mitigating frequency fluctuation. Moreover, a novel event-based protection scheme for a multi-terminal DC power system has been developed and evaluated. In this technique, fault identification and classifications are performed based on the current derivative method and employing an artificial inductive line impedance. The developed scheme does not require high speed communication and synchronization and it transfers much less data when compared with the traditional method such as the differential protection approach. Moreover, this scheme utilizes less measurement equipment since only the DC bus data is required.

Using pellet fuels for residential heating: A field study on its efficiency and the users’ satisfaction

Electricity, gas, heat integration via residential hybrid heating technologies – An investment model assessment

During 1975–1980, U.S. solar policy emphasized financial incentives to potential purchasers as the primary means of stimulating the introduction and spread of residential solar heating systems. This article examines the importance of nonfinancial factors in decisions to purchase residential solar heating systems during these early stages of market penetration and discusses the implications these factors have for policy design. Drawing upon research on the diffusion of innovations, on the effectiveness of income tax credits for solar heating systems, and on solar energy system purchasing decisions themselves, the argument is developed that nonfinancial factors such as system reliability, warranty protection, environmental concerns, adequate information about system costs and performance, and confidence in system suppliers and installers are at least as important as initial system cost to early purchasers. These considerations were not reflected in U.S. solar policy to the extent warranted. As a result, that policy failed to promote the balanced development of all elements essential to a viable residential solar heating industry and probably failed to alter the intentions of many prospective solar system purchasers. The reasons U.S. policymakers were relatively insensitive to nonfinancial factors are discussed and an alternative strategy for increasing the rate of market penetration of residential solar heating systems is offered.

Data-Driven Methodology for Energy and Peak Load Reduction of Residential HVAC Systems

Current refrigeration and air conditioning systems are mostly based on the vapor compression cycle, which require electrical energy input. Absorption systems have gained new interest due to the possibility of utilizing waste heat as energy input. In addition, the environmental impact generated by such systems is recognized as much smaller than vapor compression systems. Therefore, this work developed and characterized an absorption refrigeration system with an innovative generator level optical control and variable working fluid mass flow rate, with potential for use in industrial, commercial and residential heating, ventilation, air conditioning, and refrigeration (HVAC & R) systems. The system is hybrid, since it was designed to be fed with heat from the burning of different fuels and/or waste heat sources in complementary fashion. The system consists of: a condenser, an evaporator, two expansion valves, two absorbers, a centrifugal pump, a regenerative heat exchanger, a generator, a rectifier, a generator level optical control system, and two liquid accumulators. The developed level control system consists of 3 light Dependent Resistors (LDR) positioned inside a box built around a transparent level meter, and illuminated internally by a low power light bulb. A frequency inverter and a centrifugal pump allow for the working fluid solution inside the generator to be within a safe range for efficient cooling cycle operation. The system measured refrigeration capacity rate was 2.3 TR, which qualifies as a good performance, since the equipment was originally designed for 1 TR.

Effects of dead-band and set-point settings of on/off controllers on the energy consumption and equipment switching frequency of a residential HVAC system

Supervisory model predictive controller (MPC) for residential HVAC systems: Implementation and experimentation on archetype sustainable house in Toronto