District heating and cooling optimization and enhancement – towards integration of renewables, storage and smart grid

District heating and cooling (DHC) systems are attracting increased interest for their low carbon potential. However, most DHC systems are not operating at the expected performance level. Optimization and Enhancement of DHC networks to reduce (a) fossil fuel consumption, CO2 emission, and heat losses across the network, while (b) increasing return on investment, form key challenges faced by decision makers in the fast developing energy landscape. While the academic literature is abundant of research based on field experiments, simulations, optimization strategies and algorithms etc., there is a lack of a comprehensive review that addresses the multi-faceted dimensions of the optimization and enhancement of DHC systems with a view to promote integration of smart grids, energy storage and increased share of renewable energy. The paper focuses on four areas: energy generation, energy distribution, heat substations, and terminal users, identifying state-of-the-art methods and solutions, while paving the way for future research

Optimization approaches for exploiting the load flexibility of electric heating devices in smart grids

Energy systems all over the world are undergoing a fundamental transition to tackle climate change and other environmental challenges. The share of electricity generated by renewable energy sources has been steadily increasing. In order to cope with the intermittent nature of renewable energy sources, like photovoltaic systems and wind turbines, the electrical demand has to be adjusted to their power generation. To this end, flexible electrical loads are necessary. Moreover, optimization approaches and advanced information and communication technology can help to transform the traditional electricity grid into a smart grid. To shift the electricity consumption in time, electric heating devices, such as heat pumps or electric water heaters, provide significant flexibility. In order to exploit this flexibility, optimization approaches for controlling flexible devices are essential. Most studies in the literature use centralized optimization or uncoordinated decentralized optimization. Centralized optimization has crucial drawbacks regarding computational complexity, privacy, and robustness, but uncoordinated decentralized optimization leads to suboptimal results. In this thesis, coordinated decentralized and hybrid optimization approaches with low computational requirements are developed for exploiting the flexibility of electric heating devices. An essential feature of all developed methods is that they preserve the privacy of the residents. This cumulative thesis comprises four papers that introduce different types of optimization approaches. In Paper A, rule-based heuristic control algorithms for modulating electric heating devices are developed that minimize the heating costs of a residential area. Moreover, control algorithms for minimizing surplus energy that otherwise could be curtailed are introduced. They increase the self-consumption rate of locally generated electricity from photovoltaics. The heuristic control algorithms use a privacy-preserving control and communication architecture that combines centralized and decentralized control approaches. Compared to a conventional control strategy, the results of simulations show cost reductions of between 4.1% and 13.3% and reductions of between 38.3% and 52.6% regarding the surplus energy. Paper B introduces two novel coordinating decentralized optimization approaches for scheduling-based optimization. A comparison with different decentralized optimization approaches from the literature shows that the developed methods, on average, lead to 10% less surplus energy. Further, an optimization procedure is defined that generates a diverse solution pool for the problem of maximizing the self-consumption rate of locally generated renewable energy. This solution pool is needed for the coordination mechanisms of several decentralized optimization approaches. Combining the decentralized optimization approaches with the defined procedure to generate diverse solution pools, on average, leads to 100 kWh (16.5%) less surplus energy per day for a simulated residential area with 90 buildings. In Paper C, another decentralized optimization approach that aims to minimize surplus energy and reduce the peak load in a local grid is developed. Moreover, two methods that distribute a central wind power profile to the different buildings of a residential area are introduced. Compared to the approaches from the literature, the novel decentralized optimization approach leads to improvements of between 0.8% and 13.3% regarding the surplus energy and the peak load. Paper D introduces uncertainty handling control algorithms for modulating electricheating devices. The algorithms can help centralized and decentralized scheduling-based optimization approaches to react to erroneous predictions of demand and generation. The analysis shows that the developed methods avoid violations of the residents\u27 comfort limits and increase the self-consumption rate of electricity generated by photovoltaic systems. All introduced optimization approaches yield a good trade-off between runtime and the quality of the results. Further, they respect the privacy of residents, lead to better utilization of renewable energy, and stabilize the grid. Hence, the developed optimization approaches can help future energy systems to cope with the high share of intermittent renewable energy sources

Application of heat pumps and thermal storage systems for improved control and performance of microgrids

The high penetration of renewable energy sources (RES), in particular, the rooftop photovoltaic (PV) systems in power systems, causes rapid ramps in power generation to supply load during peak-load periods. Residential and commercial buildings have considerable potential for providing load exibility by exploiting energy-e_cient devices like ground source heat pump (GSHP). The proper integration of PV systems with the GSHP could reduce power demand from demand-side. This research provides a practical attempt to integrate PV systems and GSHPs e_ectively into buildings and the grid. The multi-directional approach in this work requires an optimal control strategy to reduce energy cost and provide an opportunity for power trade-o_ or feed-in in the electricity market. In this study, some optimal control models are developed to overcome both the operational and technical constraints of demand-side management (DSM) and for optimum integration of RES. This research focuses on the development of an optimal real-time thermal energy management system for smart homes to respond to DR for peak-load shifting. The intention is to manage the operation of a GSHP to produce the desired amount of thermal energy by controlling the volume and temperature of the stored water in the thermal energy storage (TES) while optimising the operation of the heat distributors to control indoor temperature. This thesis proposes a new framework for optimal sizing design and real-time operation of energy storage systems in a residential building equipped with a PV system, heat pump (HP), and thermal and electrical energy storage systems. The results of this research demonstrate to rooftop PV system owners that investment in combined TSS and battery can be more profitable as this system can minimise life cycle costs. This thesis also presents an analysis of the potential impact of residential HP systems into reserve capacity market. This research presents a business aggregate model for controlling residential HPs (RHPs) of a group of houses that energy aggregators can utilise to earn capacity credits. A control strategy is proposed based on a dynamic aggregate RHPs coupled with TES model and predicting trading intervals capacity requirements through forecasting demand and non-scheduled generation. RHPs coupled with TES are optimised to provide DSM reserve capacity. A rebound effect reduction method is proposed that reduces the peak rebound RHPs power

Day-ahead optimization of integrated electricity and thermal system combining multiple types of demand response strategies and situation awareness technology

Under the dual pressure of energy shortage and environmental pollution, relying only on increasing the installed capacity of units and line transmission capacity cannot cope with the conflict between the growth of power demand and the difficulty of grid expansion in the long run. Demand response conducts users to change their energy consumption habits through system-issued electricity prices or incentives, so that the demand of the load side can be adjusted flexibly, which can further enhance the consumption of wind power and improve system economics. Based on the background of diversified energy use, this paper proposes a day-ahead optimal scheduling strategy for integrated electricity and thermal system considering multiple types of demand response. Firstly, the dispatch framework of integrated electricity and thermal system with the situation awareness technology is constructed to address uncertainties of Renewable Energy Sources, thus helping system mitigate uncertain risks. Secondly, the demand response mechanism of power system and regional thermal inertia of thermal system are modeled, respectively, to uncover the principles of load regulation of different energy systems; Then, a day-ahead optimal scheduling model for the integrated thermal and electricity system is developed, and the consumption evaluation index is integrated to indicate energy utilization efficiency; Finally, a combined electric-heat system model with 39-node grid and 6-node heat network is developed, and the positive effects of considering multiple types of demand response and situation awareness technology on promoting the consumption of renewable energy and improving the energy efficiency of the system are verified through the case study

Heat Pumps and Their Role in Decarbonising Heating Sector: A Comprehensive Review. ESRI WP627, June 2019

Addressing the growing concerns of climate change necessitates the decarbonisation of energy sectors globally. The heating sector is the largest energy end-use, accounting for almost half of the total energy consumption in most countries. This paper presents an extensive review of previous works on several aspects of heat pumps, including their role in the decarbonisation of the heating sector. In addition, we cover themes related to the recent technological advances of heat pumps as well as their roles in terms of adding flexibility to renewable-rich systems and carbon abatement. We also identify challenges and barriers for a significant uptake of heat pumps in various markets. Generally, as the share of renewables in the energy mix increases, heat pumps can play a role in addressing a multitude of problems induced by climate change. However, economic, regulatory, structural and infrastructural barriers exist, which may hinder heat pump integration rate

An efficient water flow control approach for water heaters in direct load control

Tank water heaters (WHs) are present in a prevailing number of European households. Serving as energy buffers WHs have come under the spotlight of various direct load control (DLC) programs over the last few decades. Although DLC has proven to be an efficient measure towards daily peak demand shaving, the payback effect might lead to a new peak in the grid. This payback phenomenon takes place every time a group of WHs under DLC is permitted to catch up. If not handled properly. This paper presents a novel real-time water flow control approach for domestic water heating systems aiming at decreasing the payback effect of DLC actions. We identify possible control strategies based on an analysis of the water system's thermal dynamics. We formulate the problem of optimal water flow control in terms of minimum WH payback demand and maximum user comfort satisfaction. User comfort is formalized by an integral energy characteristic. Simulations show that water flow control can significantly mitigate the DLC payback effect by reaching the fair compromise between energy savings and discomfort of an end-user

Tilalämmityksen kysyntäjousto mallipohjaisella algoritmilla toimistorakennuksessa

Decreasing the CO2 emissions of building stock plays a remarkable role in the mitigation of global warming. The share of building sector from both the global final energy use and CO2 emissions is about 30%. Demand response of electricity and district heating provides one tool for decreasing emissions in the whole energy system. In demand response the buildings energy use is controlled so that the peak-load consumption in the energy grid decreases and the consumption profile stabilizes. CO2 emissions are reduced since the need for emission-intensive peak-demand generation decreases. The building owners benefit from the energy cost savings and the energy producers from the higher grid efficiency and decreased investments for peak-demand power plants. The main objective of this thesis was to define the potential of space heating demand response in the perspective of local thermal comfort, cost savings and energy flexibility. Demand response was implemented using a model predictive control algorithm (MPC) that optimized and controlled the space heating temperature setpoints. The MPC algorithm was tested with dynamical simulation model of an educational office building located in Aalto University campus area. The second research question was to examine how the demand response of space heating affects the local thermal comfort of occupants. The draught risk during the demand response was investigated by thermal manikin measurements in workstations near windows. To prevent the draught risk, a window surface temperature restriction was implemented in the MPC control algorithm and its influence on the demand response potential was investigated with different properties of windows. The thermal comfort measurements showed that the draught risk increased in workstations adjacent to windows during the decreased heating power. The increase in draught risk was noticed when the window surface temperature dropped below 15 °C while the heating was turned OFF. The influence from the window surface temperature restriction on the demand response potential was found to be small. With energy efficient windows, the influence was negligible and with non-energy efficient windows the demand response potential was affected only when unnecessary high power requirements were set. Using the MPC algorithm, the annual heating cost of the case building could be decreased 4.7%. The highest energy flexibility obtained was 14%.Rakennusten hiilidioksidipäästöjen vähentämisellä voidaan edistää merkittävästi ilmastonmuutoksen torjumista, sillä rakennusten osuus kokonaisenergiankulutuksesta (ja hiilidioksidipäästöistä) maailmassa on noin 30%. Sähkön ja lämmön kysyntäjousto rakennuksissa on yksi keino koko energiajärjestelmän kasvihuonepäästöjen vähentämiseen. Kysyntäjoustossa kuluttajat muuttavat kulutustaan siten, että energiaverkon huipputehon tarve laskee ja kulutuksesta tulee stabiilimpaa. Kysyntäjousto vähentää kasvihuonepäästöjä, sillä energia- ja päästöintensiivisiä huippuvoimalaitosten käyttötarve vähenee. Kysyntäjoustosta on hyötyä rakennusten omistajille kustannussäästöjen muodossa ja energiayhtiöille investointitarpeen pienenemisenä sekä verkon hyötysuhteen paranemisena. Tämän tutkimuksen tavoitteena oli tutkia tilojen lämmityksen kysyntäjoustopotentiaalia kustannussäästöjen, energiankäytön joustavuuden ja lämpöviihtyvyyden näkökulmasta. Lämmityksen kysyntäjousto toteutettiin tilojen lämmitystä ohjaavan mallipohjaisen algoritmin avulla. Algoritmia testattiin Aalto yliopiston kampusalueella sijaitsevaan opetusrakennukseen dynaamisen simulointityökalun avulla. Toisena tutkimuskysymyksenä oli selvittää millainen vaikutus lämmityksen kysyntäjoustolla on lokaaliin lämpöviihtyvyyteen. Tässä työssä kysyntäjouston vaikutusta vetoriskiin tutkittiin kokeellisesti lämpönuken avulla työpisteissä, jotka sijaitsivat ikkunoiden lähellä. Kylmistä ikkunapinnoista johtuvan vetoriskin välttämiseksi kysyntäjoustolle asetettiin rajoite mallipohjaisessa algoritmissa, jonka vaikutusta kysyntäjoustopotentiaaliin tutkittiin erilaisilla ikkunoiden ominaisuuksilla. Kokeelliset lämpöviihtyvyysmittaukset osoittivat, että vetoriski ikkunoiden lähellä sijaitsevissa toimistopisteissä kasvaa, kun pattereiden tehoa lasketaan kysyntäjouston aikana. Vetoriskin huomattiin kasvavan, mikäli ikkunan pintalämpötila laski alle 15 °C, kun patterit eivät olleet päällä. Vetoriskin pienentämiseksi tehdyn rajoitteen vaikutus kysyntäjoustolla saavutettaviin kustannussäästöihin sekä energiajoustavuuteen huomattiin olevan pieni. Energiatehokkailla ikkunoilla vaikutus kysyntäjoustopotentiaaliin oli mitätön, ja huonoilla (U-arvo = 2,6 W/m2K) ikkunoilla potentiaali laski vasta tarpeettoman suurilla lämmitystehon korotuksilla. Mallipohjaisen algoritmin avulla tutkitun toimistorakennuksen vuotuisia lämmityskustannuksia pystyttiin vähentämään noin 4.7%. lämmityksen joustavuudeksi saatiin parhaassa tapauksessa 14%