Modelling and design of local energy systems incorporating heat pumps, thermal storage, future tariffs, and model predictive control

The planning-level design of local energy systems requires sufficiently capable modelling tools which incorporate heat pumps, thermal storage, future electricity markets, and predictive control strategies. Gaps were identified in a review of existing local energy system tools: (i) ability to adapt and access source code; (ii) temperature dependence for heat pump models; (iii) stratification model for thermal storage models; (iv) modelling of evolving electricity markets; and (v) ability to explore predictive controls. A novel modelling tool, PyLESA, has been developed to tackle these gaps and to explore predictive and non-predictive controls, and existing and future electricity tariffs. PyLESA possesses the following modelling capabilities: resources, and electrical and heat demands; electricity production; heat pump; hot water tank; electricity tariffs; fixed order control (FOC); model predictive control (MPC); and KPIs. A sizing study for a proposed design of a district heating network was devised to showcase an application of PyLESA. Aims were to compare control strategies and electricity tariffs, and to identify an optimal size combination of heat pump and hot water tank. Comparisons between control strategies found that MPC offers savings over FOC. The lowest levelized cost of heat for the existing electricity tariffs was for the time-of-use tariff with MPC, 750kW heat pump and 500m3 hot water tank. A wind tariff, with a 1000kW heat pump and 2000m3 hot water tank, benefits from using MPC over the FOC: levelized heat costs reduce by 41.1%, and heat demand met by RES increases from 52.8% to 70.2%. It is shown that the proposed design can be sized using existing electricity tariffs, and additional hot water tank capacity added later to benefit from future tariffs. The results convey the advantage of combining flexible tariffs with optimally sized thermal storage and showcase PyLESA as capable of usefully aiding the design of local energy systems.The planning-level design of local energy systems requires sufficiently capable modelling tools which incorporate heat pumps, thermal storage, future electricity markets, and predictive control strategies. Gaps were identified in a review of existing local energy system tools: (i) ability to adapt and access source code; (ii) temperature dependence for heat pump models; (iii) stratification model for thermal storage models; (iv) modelling of evolving electricity markets; and (v) ability to explore predictive controls. A novel modelling tool, PyLESA, has been developed to tackle these gaps and to explore predictive and non-predictive controls, and existing and future electricity tariffs. PyLESA possesses the following modelling capabilities: resources, and electrical and heat demands; electricity production; heat pump; hot water tank; electricity tariffs; fixed order control (FOC); model predictive control (MPC); and KPIs. A sizing study for a proposed design of a district heating network was devised to showcase an application of PyLESA. Aims were to compare control strategies and electricity tariffs, and to identify an optimal size combination of heat pump and hot water tank. Comparisons between control strategies found that MPC offers savings over FOC. The lowest levelized cost of heat for the existing electricity tariffs was for the time-of-use tariff with MPC, 750kW heat pump and 500m3 hot water tank. A wind tariff, with a 1000kW heat pump and 2000m3 hot water tank, benefits from using MPC over the FOC: levelized heat costs reduce by 41.1%, and heat demand met by RES increases from 52.8% to 70.2%. It is shown that the proposed design can be sized using existing electricity tariffs, and additional hot water tank capacity added later to benefit from future tariffs. The results convey the advantage of combining flexible tariffs with optimally sized thermal storage and showcase PyLESA as capable of usefully aiding the design of local energy systems

Heat pump and thermal storage sizing with time-of-use electricity pricing

Heat pump and thermal storage sizing studies require modelling to ensure capital and operational costs are minimised. Modelling should consider added flexibility, eg grid services, sector coupling benefits, eg utilising excess wind production, and access to electricity markets, eg time-of-use tariffs. This paper presents a two-step methodology for sizing heat pump and thermal storage systems with a time-of-use electricity tariff. The first step is a modelling method for decentralised energy systems, with the broader aim of assisting planning-level design, and consists of resource assessment, demand assessment, electrical components, thermal components, storage components, and control strategies. The second step is a parametric analysis of heat pump and thermal storage size combinations. This is then applied to a sizing study for an existing residential district heating network including a time-of-use electricity tariff. The performance metrics:% of heat pump thermal output at low-cost period,% of heat demand met by heat pump, electricity import cost, and capital cost, were plotted and tabulated to compare sizing combinations. Graphs explore the operation of the heat production units and the thermal storage. Future development involving use of model predictive control and grid services, and alternative applications including operational planning and feasibility studies, are then discussed

A methodology for designing decentralised energy systems with predictive control for heat pumps and thermal storage

Decentralised energy systems provide the potential for adding energy system flexibility by separating demand/supply dynamics with demand side management and storage technologies. They also offer an opportunity for implementing technologies which enable sector coupling benefits, for example, heat pumps with controls set to use excess wind power generation. Gaps in this field relating to planning-level modelling tools have previously been identified: thermal characteristic modelling for thermal storage and advanced options for control. This paper sets out a methodology for modelling decentralised energy systems including heat pumps and thermal storage with the aim of assisting planning-level design. The methodology steps consist of: 1) thermal and electrical demand and local resource assessment methods, 2) energy production models for wind turbines, PV panels, fuel generators, heat pumps, and fuel boilers, 3) bi-directional energy flow models for simple electrical storage, hot water tank thermal storage with thermal characteristics, and a grid-connection, 4) predictive control strategy minimising electricity cost using a 24-hour lookahead, and 5) modelling outputs. Contributions to the identified gaps are examined by analysing the sensible thermal storage model with thermal characteristics and the use of the predictive control. Future extensions and applications of the methodology are discussed

A modelling tool selection process for planning of community scale energy systems including storage and demand side management

Storage and Demand Side Management (DSM) are key in integrating renewable energy into community energy systems. There are many modelling tools which support design of such systems. In order to select an appropriate tool it is essential to understand tool capabilities and assess how these match requirements for a specific situation. The aim of this paper is to provide a process to be used to make such a selection consisting of: (i) a tool capability categorisation, (ii) a stepwise tool selection process. Capabilities of 13 tools (screened from 51) for community scale were categorised covering: input data characteristics; supply technologies; design optimisation; available outputs; controls and DSM; storage; and practical considerations. A stepwise selection process is defined, adapted from software engineering, in which tools are scored based on 'essential', 'desirable', or 'not applicable' technical capabilities for the specific situation. Tools without essential capabilities are eliminated. Technical scores and practical considerations are then used to select the tool. The process is demonstrated for a simple case study. The future applicability of the selection process is discussed. Findings from the capability categorisation process are highlighted including gaps to be addressed and future trends in modelling of such systems

Towards a smart community evaluation and implementation toolkit - low-cost mini-district predictive controls with flexible tariffs

The drive to decarbonise the grid through intermittent generation requires an increase in system flexibility. To achieve this all energy assets, regardless of size and location, need to be incentivised to contribute. For smaller and remote assets and microgrids, the ability to participate in the current third-party flexibility markets remains limited. A toolkit has therefore been developed to allow assessment for smaller systems to use standard flexible energy tariffs, orchestrated by a simple, independent locally-situated controller, to achieve financial benefits and assist grid balancing. The toolkit has demonstrated that significant savings are achievable for a small mini-district scheme. The integrated Python-based optimisation engine can be used on low-cost platforms, such as the Raspberry Pi, which indicates that the developed algorithms has the potential to orchestrate microgrids as part of an integrated control system

Evolution of the Thrombolytic Treatment Window for Acute Ischemic Stroke

Ischemic stroke is a major cause of morbidity and mortality for which the only approved treatment in the acute setting is intravenous thrombolysis. The efficacy and safety of recombinant tissue plasminogen activator (rt-PA) have been firmly established within 3 h of symptom onset; however, few patients are eligible for treatment in this time window. Expanding the time for treatment has been challenging, but new evidence has demonstrated a modest statistical improvement in selected patients when rt-PA is administered within 4.5 h. This important finding hopefully will enable more patients to receive treatment and simultaneously provides an opportunity to reaffirm that the benefits of rt-PA diminish with time

Anti-angiogenic therapy for cancer: Current progress, unresolved questions and future directions

Tumours require a vascular supply to grow and can achieve this via the expression of pro-angiogenic growth factors, including members of the vascular endothelial growth factor (VEGF) family of ligands. Since one or more of the VEGF ligand family is overexpressed in most solid cancers, there was great optimism that inhibition of the VEGF pathway would represent an effective anti-angiogenic therapy for most tumour types. Encouragingly, VEGF pathway targeted drugs such as bevacizumab, sunitinib and aflibercept have shown activity in certain settings. However, inhibition of VEGF signalling is not effective in all cancers, prompting the need to further understand how the vasculature can be effectively targeted in tumours. Here we present a succinct review of the progress with VEGF-targeted therapy and the unresolved questions that exist in the field: including its use in different disease stages (metastatic, adjuvant, neoadjuvant), interactions with chemotherapy, duration and scheduling of therapy, potential predictive biomarkers and proposed mechanisms of resistance, including paradoxical effects such as enhanced tumour aggressiveness. In terms of future directions, we discuss the need to delineate further the complexities of tumour vascularisation if we are to develop more effective and personalised anti-angiogenic therapies. © 2014 The Author(s)

Planning level sizing of heat pumps and hot water tanks incorporating model predictive control and future electricity tariffs

Heat pumps and hot water tanks in local energy systems require sizing to increase on-site renewables self-consumption; decrease costs through variable electricity pricing; and utilise low-cost wind power. While detailed tools can capture these mechanisms, planning-level tools lack functionality and miss these benefits. In this paper an open-source planning-level modelling tool, PyLESA, is presented and applied to a sizing study to demonstrate the capturing of these benefits at the planning-level. Specific aims of the study were to investigate: (i) model predictive control vs. fixed order control, (ii) existing and future wind-influenced electricity tariffs, and (iii) optimal cost size combinations of heat pump and hot water tank. The lowest levelized cost of heat for the existing tariffs was for a time-of-use tariff, 750 kW heat pump and 500 m 3 hot water tank combination. For the future wind-influenced tariff a 1000 kW heat pump and 2000 m 3 hot water tank was cost optimal and showed model predictive control benefits over fixed order control with levelized heat costs reducing 41 %, and heat demand met by renewables increasing 18 %. These results demonstrate PyLESA as capable of capturing flexibility benefits at the planning stage of design and quantify the advantage of combining flexible tariffs with model predictive control