An Innovative Control Framework for District Heating Systems: Conceptualisation and Preliminary Results

This paper presents a holistic innovative solution for the transformation of the current district heating and cooling systems to automated more efficient systems. A variety of technological advancements have been developed and integrated to support the effective energy management of future district heating and cooling sector. First, we identify and discuss the main challenges and needs that are in line with the EU objectives and policy expectations. We give an overview of the main parts that our solution consists of, with emphasis on the forecasting tools and an advanced control system that addresses unit commitment and economic load dispatch problems. The proposed control approach employs distributed and scalable optimisation algorithms for optimising the short-term operations of a district heating and cooling plant subject to technical constraints and uncertainties in the energy demand. To test the performance and validate the proposed control system, a district heating plant with multiple energy generation units and real-life heat load data were used. Simulation experiments were also used to evaluate the benefits of using thermal storage units in district heating systems. The results show that the proposed method could achieve significant cost savings when energy storage is employed. The proposed control strategy can be applied for both operating optimally district heating plants with storage and supporting investment planning for new storage units

development and application of a predictive controller to a mini district heating network fed by a biomass boiler

Abstract Energy saving is actually recognized as one of the most significant ways to reduce primary energy consumption and pollutant emissions. Due to the remarkable importance of heating systems and heat distribution grids, Siram by Veolia and the University of Parma have developed an optimal control system for District Heating Networks. Usually building control systems are designed to manage plants relying on past experience: the optimal control system described in the paper defines plant management strategy on the basis of the future behavior of the systems and the external environment. The proposed control system has been applied to the heating system and the distribution network of a school complex in Podenzano (Emilia-Romagna region). The district heating supplies heat to three different buildings (primary school, secondary school and sports hall). The heating plant is composed of three generators (two fed by natural gas and one by wooden biomass), a Thermal Energy Storage, two main distribution manifolds (supply and return) and three secondary circuits, which distribute heat to the buildings. In the first part of the paper the control algorithm is described, split into plant simulation models and the optimization algorithm. In the second part, the real application and the new communication architecture applied on site are outlined and, finally, the obtained results are reported highlighting the management strategies of generators and pumps. The optimal control strategy application gave important results in terms of energy saving, in particular the energy supplied to the buildings dropped significantly, this is the result of knowing the building behavior in advance

a model in the loop application of a predictive controller to a district heating system

Abstract The high weather variability due to climate change and the need to reduce carbon emissions require innovative solutions for energy systems and grids. In particular, improvements in control strategies allow to increase efficiency without changing the system configuration. Adaptive controllers, as currently proposed, base the management of the system on its past behavior. The main drawback of these methods is the lack of flexibility required to face the mentioned scenario. This paper presents a Model Predictive Control approach which, instead, is based on the prediction of the future evolution of the controlled system. Since it allows to consider the external conditions variability, a more resilient way to manage District Heating and Cooling networks can be achieved. The novel control strategy is developed and tested through a Model-in-the-Loop application to a thermal energy network. This latter is composed by combining physics-based dynamic models from a dedicated library of energy systems components developed by the authors in the Matlab®/Simulink® environment. The network model is controlled by the MPC controller model, which shows to be flexible and reliable in the optimization and management of energy systems

Experimental demonstration of a Smart Homes Network in Rome

According to the European Strategy Energy Technology (SET) Plan, the resident-user engagement into the national energy strategy is pivotal to the project as it is considered to be one of the most important challenges. The Italian Minister of Economic Development and ENEA have entered into a Programme Agreement for the execution of the research and development lines of General Interest for the National Electricity System. In particular, as part of the “Development of an integrated model of the Urban Smart District” a Smart Home network experimentation has been carried out in the Centocelle district of Rome. This project aims to develop a replicable model able to monitor energy consumption, the degree of comfort and safety in residential buildings and to transmit raw data to a higher level ICT platform where they are analysed and aggregated to provide the user and the community with a series of constructive and valuable feedback that can shed light on their usage patterns and what ought to be improved to increase their energy awareness. The heart of the system is the Energy Box (EB) that allows the control of the devices (sensors and actuators) and transforms each and every home into an active node of a smart network, which allows the user to share data and information with the outside world, as to increase the dwellings' sense of participation and belonging to the community by providing them with useful tools for new forms of interaction. In perspective, the system architecture developed in the project will also be able to transform each user from a simple consumer into an active participant in the energy market, allowing individual residences to control demand (demand-side management). Finally, the brand-new home digital infrastructure has paved the way to a series of additional services, such as assisted living and home security

Operational supply and demand optimisation of a multi-vector district energy system using artificial neural networks and a genetic algorithm

Decentralisation of energy generation and distribution to local districts or microgrids is viewed as an important strategy to increase energy efficiency, incorporate more small-scale renewable sources and reduce greenhouse gas emissions. To achieve these goals, an intelligent, context-aware, adaptive energy management platform is required. This paper will demonstrate two district energy management optimisation strategies; one that optimises district heat generation from a multi-vector energy centre and a second that directly controls building demand via the heating set point temperature in addition to the heat generation. Several Artificial Neural Networks are used to predict variables such as building demand, solar photovoltaic generation, and indoor temperature. These predictions are utilised within a Genetic Algorithm to determine the optimal operating schedules of the heat generation equipment, thermal storage, and the heating set point temperature. Optimising the generation of heat for the district led to a 44.88% increase in profit compared to a rule-based, priority order baseline strategy. An additional 8.04% increase in profit was achieved when the optimisation could also directly control a proportion of building demand. These results demonstrates the potential gain when energy can be managed in a more holistic manner considering multiple energy vectors as well as both supply and demand

Continuous Commissioning® and Energy Management Control Strategies at Alamo Community College District

H&H Report: Continuous Commissioning at Alamo Community College DistrictThis paper presents an overview of energy savings through the optimization of facility Heating, Ventilation, and Air Conditioning (HVAC) systems for the college campuses of the Alamo Community College District. This Continuous Commissioning® process includes energy management control strategies that focus on utility rate structures. Detailed commissioning activities of the College district and Central Plants are discussed and documented, and overall savings are provided. Continuous Commissioning® (CC®) of the Alamo Community College District (ACCD) main campuses located in San Antonio, Texas began in June 2002. The three largest campuses, San Antonio College, St. Philips College, and Palo Alto College underwent the CC® process. The project was completed in August of 2006. The success of the initial project created the opportunity to expand the CC ® process throughout the college district. Utility data was provided by the Alamo Community College District in conjunction with the local municipal utility company. ACCD’s enterprise energy strategy to achieve persistence of savings is to closely monitor savings deviation and by training facility HVAC personnel at each campus in the Continuous Commissioning® processes as well as the District-wide metering of individual building energy consumption; and the development of a strategic, District-wide Energy Management Plan

Hypothesis for a more efficient and sustainable development of a district heating in Padova, integrating renewable energies and existing generation plant

The present paper shows the background analysis to develop the optimization strategy of a neighborhood heating network sited in Padua, including it in a wider project of district renovation. The case study accounts several different end users: scholastic and offices buildings, a social housing residence and residential buildings. The analysis starts from a systematic assessment of the buildings, evaluating the need of refurbishment of the envelope and of the distribution system. Further analysis focuses on the optimization of the existing heat generation system, integrating three condensing boilers, with an air to water heat pump and a ground source heat pump, which work more efficiently during base-load periods. The management of the district heating network have been investigated using the dynamic simulation tool TRNSYS, the control strategy of the delivery temperature has been tested based on the outside temperature and verifying to satisfy comfort conditions inside the buildings. A sustainable solution is the recovery and drainage of rainwater, that can be reused for the toilets' flushing. Therefore, the project solution identified aims at a more rational use of energy sources, which is the simplest and cheapest way to proceed on the decarbonization path that is a mid-term target for the Padua administration

Robust trading strategies for a waste-to-energy combined heat and power plant in a day-ahead electricity market

© 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Waste-to-energy (WtE) technologies have been used all over the world as they can solve the dilemma of waste management, energy demand, and global warming. Many modern WtE plants are built and operated in a combined heat and power (CHP) mode due to the high overall energy efficiency. This paper studies robust trading strategies for a WtE CHP plant which sells electricity in a day-ahead electricity market and exports heat to a district heating network. Owing to the requirements of the day-ahead electricity market, plant operators must determine the trading strategy one day before real delivery of electricity. However, many key problem parameters including electricity price, heat demand, and the amount of waste delivered to the plant are uncertain at the day-ahead stage. To derive robust electricity trading strategies for the WtE CHP plant under different types of uncertainty, a two-stage robust optimization model is developed and a solution procedure based on the column-and-constraint generation method is designed. A case study is also performed to illustrate the effectiveness of the robust model and the solution procedure

A feasibility study to investigate the effectiveness and safety of an intermittent fasting diet for weight reduction in adults with Type 2 Diabetes treated with insulin : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Human Nutrition at Massey University, Albany, New Zealand

Background: Type 2 diabetes (T2DM) is the most common form of diabetes. Obesity is associated with both the development of T2DM and also the development of the complications of diabetes; increasing health care costs and morbidity and mortality. Weight loss and control of blood glucose levels should be managed with a tailored eating plan developed in negotiation between the person with diabetes and their health care team. It is essential that health care professionals are familiar with different strategies that achieve weight reduction, glycaemic and cardiovascular risk reduction goals. One emerging weight reduction strategy is fasting diets. There is currently a gap in the knowledge of whether fasting diets are an effective and safe weight reduction strategy for people with T2DM on insulin. Aim: To investigate the effectiveness and safety of an intermittent fasting diet (two day per week) as an alternative to standard dietary advice (portion control diet) for weight reduction in obese adults with insulin dependent T2DM attending a 12-week group based intervention at Waitemata District Health Board (DHB). Methods: Obese patients with T2DM treated with insulin who were attending Waitemata DHB Diabetes Service were recruited for this two arm open-label design intervention feasibility study. Both dietary strategies were implemented during a 12-week intervention at which participants received monthly dietitian-led group education and support. The intermittent fasting diet (IFD) intervention (n=8) investigated was a two day per week reduced energy intake (550-650kcal / 2300- 2700kJ per day) and five days’ usual intake making low fat choices. The portion control diet (PCD) was the comparison group (n=7) and focused on daily energy restriction through reduction in portion sizes and low fat food choices in line with current New Zealand dietary recommendations for management of T2DM. Results: Similar weight loss was achieved in both groups (IFD: 2.7 ± 3.0 kg, PCD: 1.7 ± 2.5 kg). This reduction was not significant between groups. There was a significant difference between groups in reduction in HbA1c. (P=0.003) (IFD: -11 mmol/mol, PCD: -3 mmol/mol). This decrease was significant in the IFD group only (P=0.018). Reported hypoglycaemic events were low in both groups (8 events in IFD; 21 events in PCD). Non-significant between group reductions in waist circumference (P=0.402), waist: height ratio (P=0.455), diastolic (P=0.189) and systolic blood pressure (P=0.443) were observed. Lipid profile remained stable in both groups. Conclusion: This feasibility study showed that an intermittent fasting diet can achieve similar weight loss to current standard practice dietary advice in people with T2DM. However, it is the significant reductions in HbA1c compared to a daily energy restriction diet over a three-month period seen in this study that warrant further investigation. With education from health care professionals and modification of insulin on pre-fasting and fasting day this diet may be followed safely and hypoglycaemia avoided or managed appropriately