Adjustable capability of the distributed energy system:Definition, framework, and evaluation model

The adjustable capability of distributed energy systems responding to the incentives of the upper energy supply system has been significantly improved by energy storage and renewable energy technologies. Most existing research focuses on evaluating the flexibility of distributed energy system itself or the demand response potential of end-users, but there is no specific model which takes the distributed energy system as an integrated load and evaluates its adjustable capability from the perspective of the upper energy supply system. To fill the research gap, this paper defines the adjustable capability of distributed energy systems, describes its characteristics, and proposes a unified evaluation model. Then, from the perspective of energy demand and supply sides, it quantifies the impact of each influential factor in different energy links on the adjustable capability and studies the interactive mechanism between devices within the system. Thereafter, the adjustable capability of distributed energy systems under typical scenarios at a single moment is evaluated, and the impact of economic constraints on the adjustable capability is also extensively analyzed. Accordingly, this paper proposes a sequential recurrence method to evaluate the adjustable capability of distributed energy systems against three different initial states: unknown initial state, fixed initial state, uncertain initial state. Finally, the adjustable capability concept is demonstrated on a practical industrial park to verify the effectiveness and practicability. This study deepens the connection between distributed energy systems and upper energy supply system in the Energy Internet at the energy information level. Which enables distributed energy system to quantize its energy demand range for the upper energy supply system and realize its own reliable operation and rolling optimization. In addition, this evaluation method allows upper energy supply system to plan, overhaul and dispatch more economically and reliably on the basis of understanding the energy demand of distributed energy system. Moreover, upper energy supply system can formulate the demand response strategy with the maximum revenue by balancing the size of adjustable capability interval of distributed energy system and the investment cost of demand response.</p

The State of the Art in Model Predictive Control Application for Demand Response

Demand response programs have been used to optimize the participation of the demand side. Utilizing the demand response programs maximizes social welfare and reduces energy usage. Model Predictive Control is a suitable control strategy that manages the energy network, and it shows superiority over other predictive controllers. The goal of implementing this controller on the demand side is to minimize energy consumption, carbon footprint, and energy cost and maximize thermal comfort and social welfare.&nbsp; This review paper aims to highlight this control strategy\u27s excellence in handling the demand response optimization problem. The optimization methods of the controller are compared. Summarization of techniques used in recent publications to solve the Model Predictive Control optimization problem is presented, including demand response programs, renewable energy resources, and thermal comfort. This paper sheds light on the current research challenges and future research directions for applying model-based control techniques to the demand response optimization problem

Adjustable capability of the distributed energy system:Definition, framework, and evaluation model

The adjustable capability of distributed energy systems responding to the incentives of the upper energy supply system has been significantly improved by energy storage and renewable energy technologies. Most existing research focuses on evaluating the flexibility of distributed energy system itself or the demand response potential of end-users, but there is no specific model which takes the distributed energy system as an integrated load and evaluates its adjustable capability from the perspective of the upper energy supply system. To fill the research gap, this paper defines the adjustable capability of distributed energy systems, describes its characteristics, and proposes a unified evaluation model. Then, from the perspective of energy demand and supply sides, it quantifies the impact of each influential factor in different energy links on the adjustable capability and studies the interactive mechanism between devices within the system. Thereafter, the adjustable capability of distributed energy systems under typical scenarios at a single moment is evaluated, and the impact of economic constraints on the adjustable capability is also extensively analyzed. Accordingly, this paper proposes a sequential recurrence method to evaluate the adjustable capability of distributed energy systems against three different initial states: unknown initial state, fixed initial state, uncertain initial state. Finally, the adjustable capability concept is demonstrated on a practical industrial park to verify the effectiveness and practicability. This study deepens the connection between distributed energy systems and upper energy supply system in the Energy Internet at the energy information level. Which enables distributed energy system to quantize its energy demand range for the upper energy supply system and realize its own reliable operation and rolling optimization. In addition, this evaluation method allows upper energy supply system to plan, overhaul and dispatch more economically and reliably on the basis of understanding the energy demand of distributed energy system. Moreover, upper energy supply system can formulate the demand response strategy with the maximum revenue by balancing the size of adjustable capability interval of distributed energy system and the investment cost of demand response.</p

Occupant-Facade interaction: a review and classification scheme

The interest in occupant interaction with building controls and automation systems is growing due to the wider availability of embedded sensing devices and automated or intelligent building components that can integrate building control strategies with occupant-centred data and lead to greater occupant satisfaction and reduction in energy consumption. An area of particular interest is the interaction strategies between occupants and the so called automated facades, such as dynamic shading devices and switchable glazing. Occupant-Facade interactions are often disruptive and source of dissatisfaction because of conflicts between competing requirements, e.g. energy-efficiency and indoor environmental quality. To solve these conflicts, expertise from several disciplines is required, including Behavioural Science and Building Physics, but the absence of common research frameworks impedes knowledge transfer between different fields of expertise. This paper reviews existing multi-disciplinary research on occupant interaction with facades, buildings and automation systems and provides a new classification scheme of Occupant-Facade interaction. The scheme is based on an extensive review of interactive scenarios between occupants and facades that are summarised in this paper. The classification scheme was found to be successful in: 1) capturing the multidisciplinary nature of interactive scenarios by clarifying relationships between components; 2) identifying similarities and characteristics among interactive scenarios; 3) understanding research gaps. The classification scheme proposed in this paper has the potential to be a useful tool for the multi-disciplinary research community in this field. The review also showed that more research is needed to characterise the holistic and multi-disciplinary effect of occupant interaction with intelligent building components.EPSRC Doctoral Training Accoun

Deployment and control of adaptive building facades for energy generation, thermal insulation, ventilation and daylighting: A review

A major objective in the design and operation of buildings is to maintain occupant comfort without incurring significant energy use. Particularly in narrower-plan buildings, the thermophysical properties and behaviour of their façades are often an important determinant of internal conditions. Building facades have been, and are being, developed to adapt their heat and mass transfer characteristics to changes in weather conditions, number of occupants and occupant’s requirements and preferences. Both the wall and window elements of a facade can be engineered to (i) harness solar energy for photovoltaic electricity generation, heating, inducing ventilation and daylighting (ii) provide varying levels of thermal insulation and (iii) store energy. As an adaptive façade may need to provide each attribute to differing extents at particular times, achieving their optimal performance requires effective control. This paper reviews key aspects of current and emerging adaptive façade technologies. These include (i) mechanisms and technologies used to regulate heat and mass transfer flows, daylight, electricity and heat generation (ii) effectiveness and responsiveness of adaptive façades, (iii) appropriate control algorithms for adaptive facades and (iv) sensor information required for façade adaptations to maintain desired occupants’ comfort levels while minimising the energy use

Particle swarm optimised fuzzy controller for charging–discharging and scheduling of battery energy storage system in MG applications

© 2020 The Authors Aiming at reducing the power consumption and costs of grids, this paper deals with the development of particle swarm optimisation (PSO) based fuzzy logic controller (FLC) for charging–discharging and scheduling of the battery energy storage systems (ESSs) in microgrid (MG) applications. Initially, FLC was developed to control the charging–discharging of the storage system to avoid mathematical calculation of the conventional system. However, to improve the charging–discharging control, the membership function of the FLC is optimised using PSO technique considering the available power, load demand, battery temperature and state of charge (SOC). The scheduling controller is the optimal solution to achieve low-cost uninterrupted reliable power according to the loads. To reduce the grid power demand and consumption costs, an optimal binary PSO is also introduced to schedule the ESS, grid and distributed sources under various load conditions at different times of the day. The obtained results proved that the robustness of the developed PSO based fuzzy control can effectively manage the battery charging–discharging with reducing the significant grid power consumption of 42.26% and the costs of the energy usage by 45.11% which also demonstrates the contribution of the research

An integrated model predictive control approach for optimal HVAC and energy storage operation in large-scale buildings

This paper deals with the problem of cost-optimal operation of smart buildings that integrate a centralized HVAC system, photovoltaic generation and both thermal and electrical storage devices. Building participation in a Demand-Response program is also considered. The proposed solution is based on a specialized Model Predictive Control strategy to optimally manage the HVAC system and the storage devices under thermal comfort and technological constraints. The related optimization problems turn out to be computationally appealing, even for large-scale problem instances. Performance evaluation, also in the presence of uncertainties and disturbances, is carried out using a realistic simulation framework