• Energy Research

Abstract The drive towards low carbon constructions has seen buildings increasingly utilise many different energy systems simultaneously to control the human comfort of the indoor environment; such as ventilation with heat recovery, various heating solutions and applications of renewable energy. This paper describes a dynamic modelling and simulation method (IDEAS – Inverse Dynamics based Energy Assessment and Simulation) for analysing the energy utilisation of a building and its complex servicing systems. The IDEAS case study presented in this paper is based upon small perturbation theory and can be used for the analysis of the performance of complex energy systems and also for the design of smart control systems. This paper presents a process of how any dynamic model can be calibrated against a more empirical based data model, in this case the UK Government's SAP (Standard Assessment Procedure). The research targets of this work are building simulation experts for analysing the energy use of a building and also control engineers to assist in the design of smart control systems for dwellings. The calibration process presented is transferable and has applications for simulation experts to assist in calibrating any dynamic building simulation method with an empirical based method.

Coupling the electricity and heat sectors is receiving interest as a potential source of flexibility to help absorb surplus renewable electricity. The flexibility afforded by power-to-heat systems in dwellings has yet to be quantified in terms of time, energy and costs, and especially in cases where homeowners are heterogeneous prosumers. Flexibility quantification whilst accounting for prosumer heterogeneity is non-trivial. Therefore in this work a novel two-step optimization framework is proposed to quantify the potential of prosumers to absorb surplus renewable electricity through the integration of air source heat pumps and thermal energy storage. The first step is formulated as a multi-period mixed integer linear programming problem to determine the optimal energy system, and the quantity of surplus electricity absorbed. The second step is formulated as a linear programming problem to determine the price a prosumer will accept for absorbing surplus electricity, and thus the number of active prosumers in the market. A case study of 445 prosumers is presented to illustrate the approach. Results show that the number of active prosumers is affected by the quantity of absorbed electricity, frequency of requests, the price offered by aggregators and how prosumers determine the acceptable value of flexibility provided. This study is a step towards reducing the need for renewable curtailment and increasing pricing transparency in relation to demand-side response.

Microgeneration technologies are positioned to address future building energy efficiency requirements and facilitate the integration of renewables into buildings to ensure a sustainable, energy-secure future. This paper explores the development of a robust multi-input multi-output (MIMO) controller applicable to the control of hybrid renewable microgeneration systems with the objective of minimising the electrical grid utilisation of a building while fulfilling the thermal demands. The controller employs the inverse dynamics of the building, servicing systems, and energy storage with a robust control methodology. These inverse dynamics provides the control system with knowledge of the complex cause and effect relationships between the system, the controlled inputs, and the external disturbances, while an outer-loop control ensures robust, stable control in the presence of modelling deficiencies/uncertainty and unknown disturbances. Variable structure control compensates for the physical limitations of the systems whereby the control strategy employed switches depending on the current utilisation and availability of the energy supplies. Preliminary results presented for a system consisting of a micro-CHP unit, solar PV, and battery storage indicate that the control strategy is effective in minimising the interaction with the local electrical network and maximising the utilisation of the available renewable energy.

In spite of the benefits from thermal energy storage (TES) integration in dwellings, the penetration rate in Europe is 5%. Effective fiscal policies are necessary to accelerate deployment. However, there is currently no direct support for TES in buildings compared to support for electricity storage. This could be due to lack of evidence to support incentivisation. In this study, a novel systematic framework is developed to provide a case in support of TES incentivisation. The model determines the costs, CO2 emissions, dispatch strategy and sizes of technologies, and TES for a domestic user under policy neutral and policy intensive scenarios. The model is applied to different building types in the UK. The model is applied to a case study for a detached dwelling in the UK (floor area of 122 m2), where heat demand is satisfied by a boiler and electricity imported from the grid. Results show that under a policy neutral scenario, integrating a micro-Combined Heat and Power (CHP) reduces the primary energy demand by 11%, CO2 emissions by 21%, but with a 16 year payback. Additional benefits from TES integration can pay for the investment within the first 9 years, reducing to 3.5–6 years when the CO2 levy is accounted for. Under a policy intensive scenario (for example considering the Feed in Tariff (FIT)), primary energy demand and CO2 emissions reduce by 17 and 33% respectively with a 5 year payback. In this case, the additional benefits for TES integration can pay for the investment in TES within the first 2 years. The framework developed is a useful tool is determining the role TES in decarbonising domestic energy systems.

A predictive load shifting controller has been developed and deployed in a low-carbon house near Glasgow, UK. The house features an under floor heating system, fed by an air-source heat pump. Based on forecast air temperatures and solar radiation levels, the controller 1) predicts the following day’s heating requirements to achieve thermal comfort 2) runs heat pump during off peak periods to deliver the required heat by pre-charging the under floor heating. Prior to its installation in the building, the controller’s operating characteristics were identified using a calibrated building simulation model. The performance of the controller in the house was monitored over four weeks in 2015. The monitored data indicated that the actual thermal performance of the predictive controller was better than that projected using simulation, with better levels of thermal comfort achieved. Indoor air temperatures were between 18°C to 23°C for around 87% of the time between 07:00-22:00. However, the performance of the heat pump under load shift control was extremely poor, with the heat being delivered primarily by the unit’s auxiliary immersion coil. The paper concludes with a refined version of the controller that should improve the day-ahead energy predictions and offer greater flexibility in heat pump operation for future field trials.

The efficient use of combined heat and power (CHP) systems in buildings presents a control challenge due to their simultaneous production of thermal and electrical energy. The use of thermal energy storage coupled with a CHP engine provides an interesting solution to the problem—the electrical demands of the building can be matched by the CHP engine, while the resulting thermal energy can be regulated by the thermal energy store. Based on the thermal energy demands of the building the thermal store can provide extra thermal energy or absorb surplus thermal energy production. This paper presents a multi-input multi-output inverse-dynamics-based control strategy that will minimise the electrical grid utilisation of a building, while simultaneously maintaining a defined operative temperature. Electrical demands from lighting and appliances within the building are considered. In order to assess the performance of the control strategy, a European Standard validated simplified dynamic building physics model is presented that provides verified heating demands. Internal heat gains from solar radiation and internal loads are included within the model. Results indicate the control strategy is effective in minimising the electrical grid use and maximising the utilisation of the available energy when compared with conventional heating systems.