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Hierarchical price coordination of heat pumps in a building network controlled using model predictive control
Abstract
Decarbonisation of the building sector is driving the increased use of heat pumps. As increased electrification of the heating sector leads to stress on the electricity grid, the need for district level coordination of these heat pumps emerges. This paper proposes a novel hierarchical coordination methodology, in which a price coordinator reduces the total instantaneous power demand of a building network below a power supply limit using a price signal. Each building has a model predictive controller (MPC) which maximises thermal comfort and minimises electricity costs. An additional term in the MPC objective function penalises the heat pump power demand quadratically, which when multiplied by a pseudo electricity price allows the price coordinator to reduce the peak power demand of the building network. A 2 building network is studied to analyse the price coordinator algorithm’s behaviour and demonstrate how this approach yields a trade off between comfort, energy consumption and peak demand reduction. A 100 building network case study is then presented as a proof of concept, with the price coordinator approach yielding results similar to that of a centralised controller (less than 0.7% increase in energy consumption per building per year) and a roughly fourfold decrease in computation time.
... While facing significant global warming, a lot of concerns have focused on energy use by buildings, which accounts for more than half of the total energy consumption [1,2]. Thus, a reduction in building energy consumption can lead to a reduction in CO 2 emissions. ...
... Simultaneously, the underground air temperature at the outlet of the air/water direct contact heat exchanger was also increased. Based on the values of the heating capacity and electricity consumption, the COP values were calculated using Equation (2). As can be seen in Figure 4, the COP values were ranged from 4.3 to 5.5 for the open-loop system, while the COP value was decreased from 5.0 to 2.5 for the closed-loop system as the inlet water temperature of the condenser was increased to 50 °C. ...
... For the closed-loop system, the extracted heat was decreased from 30,000 kcal/h to 18,000 kcal/h. Based on the values of the heating capacity and electricity consumption, the COP values were calculated using Equation (2). As can be seen in Figure 4, the COP values were ranged from 4.3 to 5.5 for the open-loop system, while the COP value was decreased from 5.0 to 2.5 for the closed-loop system as the inlet water temperature of the condenser was increased to 50 °C. ...
As one of the main businesses in Jeju-do in South Korea, specialized local products are grown in greenhouses. For greenhouse farming, it is preferable to use geothermal heat pump systems for energy conservation because of the stable temperature of the ground. In the same manner, heat pumps using underground air is recommended for greenhouse farming since underground air can easily be obtained from porous volcanic rocks in Jeju-do. However, direct usage of the underground air is not feasible for planting in the greenhouse or livestock care because the underground air is relatively humid and its temperature is low. For the present study, the heating performance of an air-to-water heat pump which used underground air as a heat source for greenhouse farming during the winter was assessed through measurements. In addition, the economic impact of the air-to-water heat pump (AWHP) was compared with a conventional air heater. According to the results, an AWHP can save more than 70% of the total heating costs compared with a conventional air heater. In sum, the utilization of the air-to-water heat pump using underground air can have a positive impact on reducing energy consumption as well as provide direct economic benefits.
... Rather than simply acting as a rigid demand that must be satisfied by the grid, a building can act as a useful system of components by offering the opportunity for flexibility provision (Greater London Authority, 2018). By exploiting the energy storage capacity of a building's fabric and using information about occupancy and comfort requirements, the heat/cold supplied to a building can be shifted away from times of high demand (Gonzato et al., 2019). Furthermore, storage devices including batteries (Al Essa, 2019), thermal stores (O'Dwyer et al., 2020) and electric vehicles (Barone et al., 2019), along with renewable generation sources (particularly solar generation), can be managed appropriately to allow power to be absorbed from the grid and supplied back to the grid as needed. ...
... In this manner, the digital twin concept (see for example the Gemini principles CDBB, 2018) fits with these goals. In O'Dwyer et al. (2019), the BCVTB environment is deployed as a digital twin for a distributed set of building energy and smart city assets. Using a digital representation of a system enables control strategies to be analysed in-silico prior to deployment, while running a digital twin in parallel allows for operational insights to be attained that may otherwise have been missed. ...
The power network is becoming increasingly intermittent as the contribution from renewable energy generation rises. To maintain stability and functionality of the power network, storage of renewable energies and demand-side control techniques are required. Smart grids provide the communication infrastructure to accomplish this goal. Smart grid control originated from the idea that the demand-side of the power grid can shift or shed load to reduce the strain on the network, while also maintaining consumer satisfaction and other specialist requirements.
... Rather than simply acting as a rigid demand that must be satisfied by the grid, a building can act as a useful system of components by offering the opportunity for flexibility provision (Greater London Authority, 2018). By exploiting the energy storage capacity of a building's fabric and using information about occupancy and comfort requirements, the heat/cold supplied to a building can be shifted away from times of high demand (Gonzato et al., 2019). Furthermore, storage devices including batteries (Al Essa, 2019), thermal stores (O'Dwyer et al., 2020) and electric vehicles (Barone et al., 2019), along with renewable generation sources (particularly solar generation), can be managed appropriately to allow power to be absorbed from the grid and supplied back to the grid as needed. ...
... In this manner, the digital twin concept (see for example the Gemini principles CDBB, 2018) fits with these goals. In O'Dwyer et al. (2019), the BCVTB environment is deployed as a digital twin for a distributed set of building energy and smart city assets. Using a digital representation of a system enables control strategies to be analysed in-silico prior to deployment, while running a digital twin in parallel allows for operational insights to be attained that may otherwise have been missed. ...
Smart grid control and management is a growing research area that affects the global goals of net-zero emissions, increased renewable energy generation, and efficient energy management. Active buildings are the building blocks of the smart grid of the future. Their passive role as energy consumer buildings is replaced in the new paradigm as active components, which are not only the energy consumers but also provide energy services to the net- work or neighbouring areas in time of need. This chapter puts a spotlight on the control of a community of active buildings and considers high-level smart grid control approaches that use active buildings; aggregation, frequency and voltage regulation as well as security considerations. The chapter also provides a detailed case study that considers demand-side frequency regulation, power tracking and formal control synthesis methods using energy storage systems (ESSs) and thermostatically controlled loads (TCLs).
... MPC offers the advantage of considering multiple constraints and objectives, including energy consumption, occupant comfort, and equipment limitations, to generate optimal control strategies. However, it often requires a detailed model of the building and its subsystems, which limits its applicability to individual buildings rather than clusters [9]. This overlooks the potential benefits that can be achieved through coordination and interaction between buildings in a cluster. ...
Demand response (DR) programs aim to improve grid reliability, reduce customer costs, and manage building energy demand. However, coordinating autonomous demand response in grid-interactive buildings faces challenges due to uncertainties in system model parameters, complex energy demands , and interdependent operational constraints. Specifically, the energy management problem in grid-interactive buildings is formulated as a Partially Observable Markov Decision Process (POMDP), where each building has only local information and no knowledge of other buildings' behavior. This makes achieving consistent coordinated energy management challenging. To address these issues, this paper proposes a novel approach called Sequential Update-based Multi-Agent Deep Reinforcement Learning (SUMADRL). This approach updates the policy of each building sequentially, considering the updates already made by previous buildings. This ensures that the joint policy update of buildings is always highly aligned with the overall energy optimization goal and improves coordinated energy management among buildings. Additionally, the sequential update scheme provides theoretical guarantees for achieving monotonically improving rewards and converging to Nash equilibrium, enhancing credibility and stability. Experimental results show that the proposed method significantly reduces energy costs, achieves efficient load shaping, and demonstrates superior effectiveness and stability in demand response compared to the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. Index Terms-Multi-agent deep reinforcement learning, Demand response, Grid-interactive buildings, Sequential update scheme
... [13][14][15][16][17], price for example refs. [18][19][20][21][22] and renewable energy for example refs. [23][24][25][26][27] aspects. ...
Heat pumps (HPs) are a promising option for decarbonizing the residential heating sector. Their use leads to an increased demand for electricity, which can burden the grid in uncontrolled operation, while controlled HPs can provide additional flexibility to the system. In this paper, the integration of HPs through a receding horizon approach into a power market model is presented to study the behavior of HPs considering electricity market prices and network constraints of a low‐voltage grid. In a case study with centralized and self‐consumption optimized control strategies, the effects of HPs are analyzed. Furthermore, the authors investigate how the aggregation of several households into energy communities affects the system. The results show that for the considered grid, the additional power demand of HPs is usually uncritical and a strictly cost‐minimizing operation is often possible from a grid perspective. Typically, critical grid situations occur only at times of high PV feed‐in with low HP flexibility. The formation of energy communities compared to individual households differs primarily in the allocation of cost savings to individual consumers. The difference in the impact of the two control strategies on the grid is negligible in this case.
... These factors can be changing indoor environment conditions of the building occupants, energy price fluctuations or the opportunity to better integrate new technologies such as heat pumps and thermal storage [30]. Naturally, as more intelligent building controls become prevalent in the industry, coordinating the operation of flexible loads across a large number of buildings should yield substantial benefits to the power infrastructure, such as the distribution network operators [31]. ...
Many businesses are looking for ways to improve the energy and carbon usage of their buildings, particularly through enhanced data collection and control schemes. In this context, this paper presents a case study of a food-retail building in the UK, detailing the design, installation and cost of a generalisable model predictive control (MPC) framework for its Heating, Ventilation and Air Conditioning (HVAC) system. The hardware/software solution to collect relevant data, as well as the formulation of the MPC scheme, is presented. By utilising cloud-based microservices, this approach can be applied to all modern building management systems with little upfront capital, and an ongoing monthly cost as low as 240 (1.7% compared to the baseline), while the carbon optimisation gave negligible CO2 savings due to the inability of the building to shift heating to low-carbon periods. Findings from this study indicate the potential for improving building performance via MPC strategies but impact will depend on specific building attributes.
... 2. A detailed building simulation model is created. 3. The underlying mathematical representation of the model is extracted. ...
Despite a large body of research, the widespread application of Model Predictive Control (MPC) to residential buildings has yet to be realised. The modelling challenge is often cited as a significant obstacle. This chapter establishes a systematic workflow, from detailed simulation model development to control-oriented model generation to act as a guide for practitioners in the residential sector. The workflow begins with physics-based modelling methods for analysis and evaluation. Following this, model-based and data-driven techniques for developing low-complexity, control-oriented models are outlined. Through sections detailing these different stages, a case study is constructed, concluding with a final section in which MPC strategies based on the proposed methods are evaluated, with a price-aware formulation producing a reduction in operational space-heating cost of 11%. The combination of simulation model development, control design and analysis in a single workflow can encourage a more rapid uptake of MPC in the sector.
... Some recent works [34,35] assessed the advantages to implement model predictive control (MPC) for regulating HVAC systems and controlling indoor temperature in a cluster of buildings. ...
Demand side management at district scale plays a crucial role in the energy transition process, being an ideal candidate to balance the needs of both users and grid, by managing the volatility of renewable sources and increasing energy flexibility. The presented study aims to explore the benefits of a coordinated approach for the energy management of a cluster of buildings to optimise the electrical demand profiles and provide services to the grid without penalising indoor comfort conditions. The proposed methodology makes use of a fully datadriven control scheme which exploits Long Short-Term Memory (LSTM) Neural Networks, and Deep Reinforcement Learning (DRL). A simulation environment is introduced to train a DRL controller to manage the operation of heat pumps and chilled and domestic hot water storage for a cluster of four buildings. LSTM models are trained with synthetic data set created in EnergyPlus and are integrated into simulation environment to evaluate the indoor temperature dynamics in each building. The developed DRL controller is tested against a
manually optimised Rule Based Controller (RBC). Results show that the DRL algorithm is able to reduce the overall cluster electricity costs, while decreasing the peak energy demand by 23% and the Peak to Average Ratio (PAR) by 20%, without penalizing indoor temperature control.
... Despite this, some pioneering studies have already demonstrated the advantages of a coordinated approach using the most advanced control strategies in simulated environment, including model predictive control [16], heuristic control [17] and reinforcement learning control [18]. ...
Advanced control strategies can enable energy flexibility in buildings by enhancing on-site renewable energy exploitation and storage operation, significantly reducing both energy costs and emissions. However, when the energy management is faced shifting from a single building to a cluster of buildings, uncoordinated strategies may have negative effects on the grid reliability, causing undesirable new peaks.
To overcome these limitations, the paper explores the opportunity to enhance energy flexibility of a cluster of buildings, taking advantage from the mutual collaboration between single buildings by pur- suing a coordinated approach in energy management.
This is achieved using Deep Reinforcement Learning (DRL), an adaptive model-free control algorithm, employed to manage the thermal storages of a cluster of four buildings equipped with different energy systems. The controller was designed to flatten the cluster load profile while optimizing energy con- sumption of each building. The coordinated energy management controller is tested and compared against a manually optimised rule-based one.
Results shows a reduction of operational costs of about 4%, together with a decrease of peak demand up to 12%. Furthermore, the control strategy allows to reduce the average daily peak and average peak-to- average ratio by 10 and 6% respectively, highlighting the benefits of a coordinated approach.
... This reduced choice of technology is a limitation which can be remedied by expanding on the technology options by finding the relevant data and adding them to the model. For example, alternatives such as heat recovery systems [14], thermal storage systems [25], fuel cell CHP systems [57] and dynamic energy management capabilities through advanced control systems [58], [59] are just a few examples of initiatives that could be considered by a multi-site property owner after these become more commonplace across the built environment. An additional uncertainty comes from the assumptions made for the technology costs and efficiencies that require periodical updates to ensure alignment with changing market conditions, for which we encourage a periodical review of these datasets. ...
The Paris Climate Agreement has motivated commercial organisations to set and work towards Science Based Targets, a realignment of greenhouse gas emissions in line with climate science. This work presents a modelling framework to develop cost-effective decarbonisation investment programs that address electricity and heat carbon emissions in organisations with multiple properties. The case study takes a set of 60 supermarkets in the UK and evaluates the techno-economic viability of installing biomethane combined heat and power engines and photovoltaic panels to make them zero carbon buildings. Simulation results from the batch of buildings offer the financial and environmental benefits at each site and generates a set of regression coefficients which are then applied into an optimisation problem. Solving the optimisation yields the decarbonisation investment strategy for the estate up to 2050; indicating the preferred sequence of investments the company needs to undertake to embark upon an effective low-carbon roadmap. A sensitivity analysis compliments the study to understand how market and policy externalities may influence roadmaps. Results suggest a CAPEX ranging from £57-£80 million is required to deliver an ambitious decarbonisation plan, while OPEX and carbon savings benefits range between £197 and £683 million and 461–715 ktCO2e; respectively. The case study highlights that although carbon targets can be achieved by 2030, the 2050 targets are more challenging to meet; suggesting additional technologies and policies should be considered and implemented. The framework serves as a blueprint of how modelling can assist decision-makers in reducing their carbon footprint cost-effectively to reach Science Based Targets.
... Moreover, model-based approaches are not effective for real-life implementation at large scale, due to the evolution of the environment (e.g., retrofits, PV integration), and the computational cost associated to a detailed cluster representation, that challenges control scalability. Despite this, some pioneering studies have already demonstrated the advantages of a coordinated approach using the most advanced control strategies in simulated environment, including model predictive control [11], heuristic control [12] and reinforcement learning control [13]. To overcome current limitations, Reinforcement Learning (RL), an adaptive and potentially model-free control algorithm, is used in this work. ...
Building energy management can exploit energy flexibility to increase on-site renewable energy consumption, reduce costs and provide flexibility services (i.e., load shifting, peak shaving) for the grid. However, when shifting from a single building to a cluster of buildings, an uncoordinated approach could cause new undesirable peak demands. Therefore, cluster energy flexibility needs to be explored through mutual collaboration between buildings by implementing coordinated energy management control strategies. In this paper Reinforcement Learning, an adaptive model-free control algorithm, is employed to optimally control a cluster of 4 buildings equipped with different energy system configurations. The results proved the effectiveness of the developed control strategy, in reducing both maximum and average total peak demand, highlighting the benefits of a coordinated approach with respect to a rule-based control strategy.
In this paper we discuss the differences between the industrial and academic definition of smart-grid readiness of heat pumps and investigate possibilities to bridge this gap such that existing heat pumps, to a higher degree, can participate in a smart-grid or flexibility setting. A weather compensated heat pump has been equipped to enable heating reference tracking by overriding the outdoor air temperature measurement. The reference tracking has been performed using a model predictive controller and a PI-controller. The results show that it is possible to track a heating reference but also that operating the heat pump according to smart-grid objectives has consequences, such as reduced COP and increased defrosting rate. The model predictive and PI-controller showed little difference in performance, meaning that even simple rule-based controllers can be used for tracking. Lastly, the paper provides suggestions for signals that should be available if heat pumps in the future should operate closer to the researchers’ expectations of smart-grid readiness.
Heat supply accounts for a substantial amount of terminal energy usage. However, along with price rises in primary energy, there is an urgent need to reduce the average cost of energy consumption during the purchasing of thermal services. Electric heating, an electricity-fed heating production and delivery technology, has been suggested as a promising method for improving heating efficiency, due to the ease of scheduling. However, the traditional centralized operating methods of electricity purchasing rely on explicit physical modeling of every detail, and accurate future predictions, the implementation of which are rarely practical in reality. To facilitate model-free decisions in the field of electricity purchasing, heat storage, and supply management, aimed at cost saving in a real-time price environment, this study proposes a scheduling framework based on deep reinforcement learning (DRL) and the existence of responsive users. First, the structure of a distributed heating system fed by regenerative electric boilers (REBs), which facilitate shiftable heat-load control, is introduced. A terminal heat demand response model based on thermal sensation vote (TSV), characterizing the consumption flexibility of responsive users, is also proposed. Second, due to thermal system inertia, the sequential decision problem of electric heating load scheduling is transformed into a specific Markov decision process (MDP). Finally, the edge intelligence (EI) deployed on the demand side uses a twin delayed deterministic policy gradient (TD-3) algorithm to address the action space continuity of electric heating devices. The combination of a DRL strategy and the computing power of EI enables real-time optimal scheduling. Unlike the traditional method, the trained intelligent agent makes adaptive control strategies according to the currently observed state space, thus avoiding prediction uncertainty. The simulation results validate that the intelligent agent responds positively to changes in electricity prices and weather conditions, reducing electricity consumption costs while maintaining user comfort. The adaptability and generalization of the proposed approach to different conditions is also demonstrated.
As electricity prices continue to rise, it is essential that UK non-domestic consumers develop smart energy management practices. However, most of these consumers lack awareness of the spatial–temporal dynamics of electricity prices and their tariff components. To help overcome these barriers and contribute to energy price digitalisation, this paper presents a Modelling UK Electricity Regional Costs (MUKERC) framework. A bottom-up methodology that defines all the tariff components and then aggregates them to quantify the cost of a kWh across each half-hour of the day. The framework not only facilitates understanding which tariff components have a higher impact during different time periods, but also depicts how they vary spatially across regions. This model was used to estimate and analyse the evolution of electricity costs from 2017 to 2024. Case studies from buildings in the education sector are showcased depicting their energy costs derived from their load profiles. Results show that the London area has the lowest average prices, while the Northern Wales & Merseyside is the most expensive. From the case studies conducted, peak period charges account for 17% of annual electricity costs (occurring between 4 and 7 p.m.). Winter period charges represented about 53% of the charges. The MUKERC framework showcases the valuable insights data-driven costing models offer as it allows to understand the dynamics of electricity charges and identifies “when” and “where” the cost of electricity is more expensive; thus, supporting the development of bespoke cost-effective energy measures that improve resource efficiency and smart energy management initiatives.
We highlight the key pillars of urban energy systems which would leverage on AI and digital technologies for a low carbon future. We summarise a couple of real world applications where optimisation, intelligent control systems and cloud-based infrastructure have played a transformative role in improving system performance, cost-effectiveness and decarbonisation. The case studies show that AI and digital technologies can be implemented for standalone unit operations to achieve such benefits. However, more importantly as the second case study shows, applying such technologies at a system level by integrating multiple energy vectors would give much more flexibility in terms of operation, resulting in better performance improvements and decarbonisation strategies. We conclude by highlighting the strategic trends in this fast evolving field and giving a broad outlook in terms of cost reductions and emissions savings for similar intelligent energy systems.
Buildings are responsible for a large portion of the world’s energy consumption. Any measure that can be taken to optimize the use of energy related to them must be considered. Demand Side Management (DSM) can be used to shave demand peaks and to avoid bootstrapping highly polluting fast ramp-up generators. This though brings a control problem that is complicated by the increasing diffusion of small-scale, renewable energy sources and local storage facilities which are decentralized and, in general, hard to predict reliably. The overall goal of the control strategy is to balance energy, demand/supply, and to minimize costs. This survey focuses on control strategies to support DSM, considering buildings as the load to be managed. Among the various control strategies, model predictive control (MPC) has a predominant role due to its broad applicability and easy portability to many diverse contexts. The method is suitable for any nonlinear, multi-variable, and linear parameter varying system. The survey provides a general, unifying mathematical characterization of the approaches and lays the foundations for comparing and evaluating MPC-based DSM in buildings.
Urban energy systems represent a significant proportion of global energy consumption, with residential and commercial buildings accounting for around 40%. This could be significantly reduced through the use of smarter control strategies for space heating. In district heating networks, the supply limitations associated with the electrical grid can lead to a misalignment between the locally optimal objectives of the individual dwellings and the globally optimal objective of the district. In this paper, a methodology is developed for coordinating a decentralised set of building heating subsystems within a district heating network by deriving a dynamic electricity price signal and communicating it to the network in order to satisfy a power supply limit. The price signal is transmitted by a high-level coordinator which discourages power usage when necessary. It was found that power supply limits could be satisfied in this way but only above a threshold value of 180 kW for the particular district, while lower values on the limit led to instability in the form of power shortages and occupant discomfort.
Microgrids are subsystems of the distribution grid operating as a single controllable system either connected or isolated from the grid. In this paper, a novel cooperative Model Predictive Control (MPC) framework is proposed for urban districts comprising multiple microgrids sharing certain Distributed Energy Resources (DERs). The operation of the microgrids, along with the shared DER, are coordinated such that the available flexibility sources are optimised and a common goal is achieved, e.g., minimizing energy exchanged with the distribution grid and the overall energy costs. Each microgrid is equipped with an MPC-based Energy Management System (EMS), responsible for optimally controlling flexible loads, heating systems and local generation devices based on end-user preferences, weather-dependent generation and demand forecasts, energy prices, technical and operational constraints. The proposed coordination algorithm is distributed and guarantees constraints satisfaction, cooperation among microgrids and fairness in the use of the shared resources, while addressing the issue of scalability of energy management in an urban district. Furthermore, the proposed framework guarantees an agreed cost saving to each microgrid. The described method is implemented and evaluated in a virtual testing environment that integrates accurate simulators of the microgrids. Numerical experiments show the feasibility, the computational benefits and the effectiveness of the proposed approach.
Wind power consumption is often curtailed by the inflexible operation of combined heat and power (CHP) units due to the strong coupling relationship between power generation and heating supply. Furthermore, the time delay of district heating network (DHN) and indoor temperature can heavily affects the operational plan of the district heating system (DHS). Hence, a practical CHP-DHS model and multi-regional coordinated operation strategy based on model predictive control (MPC) are developed for planning and operating this CHP system. The thermal characteristics of the demand side including the thermal inertia of buildings as well as thermal comfort are taken into consideration. Three types of heat source are considered: CHP units, electrical boilers and heat storage tanks. The objective of the optimization is to minimize the overall generation costs of CHP units and conventional thermal power units. Part of a real provincial power system and a municipal DHS in Northeastern China are used in this case study. The results indicate that the proposed method shows improved performance in wind power integration and indoor temperature control.
This paper investigates heat pump systems in smart grids, focussing on fields of application and control approaches that have emerged in academic literature. Based on a review of published literature technical aspects of heat pump flexibility, fields of application and control approaches are structured and discussed. Three main categories of applications using heat pumps in a smart grid context have been identified: First stable and economic operation of power grids, second the integration of renewable energy sources and third operation under variable electricity prices. In all fields heat pumps - when controlled in an appropriate manner - can help easing the transition to a decentralized energy system accompanied by a higher share of prosumers and renewable energy sources. Predictive controls are successfully used in the majority of studies, often assuming idealized conditions. Topics for future research have been identified including: a transfer of control approaches from simulation to the field, a detailed techno-economic analysis of heat pump systems under smart grid operation, and the design of heat pump systems in order to increase flexibility are among the future research topics suggested.
A concept of ‘cooperative’ optimization of building energy systems is proposed in this paper. A cooperative optimization framework for a group of buildings connected to heat pumps in the context of economic model predictive control is formulated. Two optimization scenarios have been considered for analysis – a ‘selfish’ optimization of an individual building and a cooperative optimization of a group of buildings. The impact of cooperative optimization on the energy usage patterns and cost of electricity for operating the heat pumps have been investigated. The proposed cooperative optimization approach is able to achieve up to 15% reduction in energy demand cost in comparison with selfish optimization. The benefits arising out of the cooperative optimization concept may be a major drive for the future smart cities and will play a significant role in advancing the concept of smart energy systems.
This paper presents an investigation of how Model Predictive Control (MPC) and weather predictions can increase the energy efficiency in Integrated Room Automation (IRA) while respecting occupant comfort. IRA deals with the simultaneous control of heating, ventilation and air conditioning (HVAC) as well as blind positioning and electric lighting of a building zone such that the room temperature as well as CO2 and luminance levels stay within given comfort ranges. MPC is an advanced control technique which, when applied to buildings, employs a model of the building dynamics and solves an optimization problem to determine the optimal control inputs. In this paper it is reported on the development and analysis of a Stochastic Model Predictive Control (SMPC) strategy for building climate control that takes into account the uncertainty due to the use of weather predictions.As first step the potential of MPC was assessed by means of a large-scale factorial simulation study that considered different types of buildings and HVAC systems at four representative European sites. Then for selected representative cases the control performance of SMPC, the impact of the accuracy of weather predictions, as well as the tunability of SMPC were investigated. The findings suggest that SMPC outperforms current control practice.
This paper presents a distributed Model Predictive Control framework based on a primal decomposition and a bundle method to control the indoor environmental conditions in a multisource/multizone building. The control aims to minimize the total energy cost under restrictions on global power consumption and local constraints on comfort and saturations on actuators. Moreover, each power source is supposed to have a time varying tarification. The distributed Model Predictive Control algorithm is based on two layers: a zone layer which is responsible of local zone decisions and a coordination layer that handles decisions that go beyond the scope of the zone. Simulation results are finally provided for a three zones building with a local power production and a changing price grid power. A computational study is also provided in order to assess the effectiveness and the real-time implementability of the proposed control method.
This paper presents a predictive control structure for thermal regulation in buildings. The proposed method exploits the intermittently operating mode of almost all types of buildings. Usually the occupation profile can be known in advance and this fact will be used to reduce the energy consumption without decreasing the thermal comfort during the occupation. For that purpose, the predictive control strategy is first presented for a single zone building then extended to a multizone building example. Two opposite control strategies commonly exists: the decentralized control structure, which does not offer good performances especially when the thermal coupling among adjacent rooms is not negligible, and on the other hand, the centralized control for which the computational demand grows exponentially with the size of the system, being very expensive for large scale buildings. Our solution is based on a distributed approach which takes the advantages of both methods mentioned above. A distributed MPC algorithm with one information exchange per time step is proposed with good control performances and low computational requirements. Simulations and a comparison performance table end the article.
The term intelligent or smart building refers to the next generation of buildings that provide new levels of comfort to the occupants with minimum possible energy consumption. They not only follow commands but also proactively learn from occupants' behavior and adapt their operation based on the indoor and outdoor conditions. These buildings are no longer solely consumers of energy, but also significant players in the ecosystem of the smart grid, in that they provide regulation services to the grid as well as energy if equipped with solar panels or other green sources. Intelligent buildings are not only safe by design but also react in the case of a fault, system malfunction, or cyber-attack to steer the system into a safe operating region. There has been much research in academia and industry towards this goal. Smart Connected Buildings Design Automation: Foundations and Trends provides the reader with a broad overview of the activities in the field of smart connected building design automation that attempts to make the vision a reality. The overarching range of such activities includes developing simulation tools for modeling and the design of buildings, and consequently control algorithms proposed to make buildings smarter and more efficient. It goes on to survey real-world and large-scale implementation of such control strategies on physical buildings. It then presents a formal co-design methodology to design buildings, taking the view that buildings are prime examples of cyber-physical systems where the virtual and physical worlds meet. It concludes by describing the growing role of buildings in the operation of the smart grid. Smart Connected Buildings Design Automation: Foundations and Trends will be of interest to industry professionals and researchers who work in the area of smart buildings, smart cities, and smart grid, with an emphasis on energy efficiency, simulation tools, optimal control, and cyber-physical systems for the emerging power markets.
Within the context of the Smart City, the need for intelligent approaches to manage and coordinate the diverse range of supply and conversion technologies and demand applications has been well established. The wide-scale proliferation of sensors coupled with the implementation of embedded computational intelligence algorithms can help to tackle many of the technical challenges associated with this energy systems integration problem. Nonetheless, barriers still exist, as suitable methods are needed to handle complex networks of actors, often with competing objectives, while determining design and operational decisions for systems across a wide spectrum of features and time-scales. This review looks at the current developments in the smart energy sector, focussing on techniques in the main application areas along with relevant implemented examples, while highlighting some of the key challenges currently faced and outlining future pathways for the sector. A detailed overview of a framework developed for the EU H2020 funded Sharing Cities project is also provided to illustrate the nature of the design stages encountered and control hierarchies required. The study aims to summarise the current state of computational intelligence in the field of smart energy management, providing insight into the ways in which current barriers can be overcome.
With the potential to decrease operating costs and improve energy efficiency, model predictive control (MPC) has been proposed as a replacement for traditional heuristic, PID, and other conventional control strategies for heating, ventilation, and air conditioning (HVAC) systems in commercial buildings. Due to the size of large commercial HVAC systems, implementing MPC as a single monolithic optimization problem is not practical nor desirable given real-time operating requirements. In this paper, we present a hierarchical decomposition for economic MPC in large-scale commercial HVAC systems using a two-layer approach. We show a sample optimization for a campus of 25 buildings with 500 total zones and a central plant consisting of eight chillers. Then, we discuss an application of the ideas presented here in the recently completed $485-million replacement of the Stanford campus heating and cooling systems and conclude with some of the control theory challenges presented by this new class of applications.
This paper describes a distributed optimization algorithm for scheduling the operation of electro-thermal heating units, such as heat pumps (HP) or electric heaters (EH) within a day-ahead (DA) operation for covering space heating demand. Flexibility for Demand Side Management (DSM) is realized through thermal storage on building level. The distributed optimization is performed offline by using the Alternating Direction Method of Multipliers algorithm (ADMM). For the distributed optimization also a suitable architecture is adapted and described. The optimization algorithm is tested within an example city district scenario and evaluated according to defined metrics.
This paper presents a flexible and modular control scheme based on distributed model predictive control (DMPC) to achieve optimal operation of decentralized energy systems in smart grids. The proposed approach is used to coordinate multiple distributed energy resources (DERs) in a low voltage (LV) microgrid and therefore, allow virtual power plant (VPP) operation. A sequential and iterative DMPC formulation is shown which incorporates global grid targets along with the local comfort requirements and performance indices. The preliminary results generated by the simulation of a studied case proves the benefits of applying such a control scheme to a benchmark low voltage microgrid.
In this paper, we discuss Economic Model Predictive Control (E-MPC) in the context of buildings with active energy storage. In particular, we propose a strategy for the optimal control of building Heating, Ventilation and Air Conditioning (HVAC) systems with chilled water thermal energy storage (TES). Owing to the multiple time scale dynamic behavior of buildings, coupled with the need to account for potentially extended forecasts of disturbances (e.g., weather, energy prices), the implementation of a centralized E-MPC must consider a relatively long prediction horizon. In turn, this results in computational difficulties that impede on real-time implementation. Computational complexity is further increased by the presence of integer decision variables, related to on/off states and operating modes in the HVAC and TES systems. In response to these challenges, we introduce a novel hierarchical E-MPC framework based on (i) establishing the optimal operation of the TES by solving a dynamic scheduling problem in the slow time scale, and (ii) using a control scheme with a shorter horizon in the fast time scale, which addresses objectives related to maintaining the indoor air temperature within comfort bounds at all times during the day. A simulation case study concerning the operation of a TES system at the University of Texas Thermal Façade Laboratory is presented, showing excellent computational and control performance.
Energy consumption by Heating Ventilation and Air Conditioning (HVAC) systems is usually heaviest when electricity prices are at their highest. The method of Economic Model Predictive Control (EMPC) can be used in conjunction with Thermal Energy Storage (TES) to time-shift power consumption away from periods of high demand to periods of low energy cost. In addition to enormous computational costs, implementation of such algorithms can result in unexpected and sometimes pathological closed-loop behavior, including inventory creep and bang-bang actuation. This paper will present an infinite-horizon formulation of the EMPC problem. While the design of this controller is achieved by a fairly simple convex optimization problem, it will be shown to alleviate many of the pathological behaviors observed in the finite-horizon case as well as significantly reduce the computational effort required for implementation. The method is illustrated on a simple building example using active TES.
This work presents a distributed model predictive (DMPC) scheme for the efficient management of energy distribution in buildings. The energy demanded by the building's residents is supplied by a renewable power system whose capacity is limited and sometimes cannot fulfill the energy requirements of the residents, depending on the availability of renewable resources. Extensions are proposed for the distributed controllers aiming to overcome difficulties that arise from the direct application of a standard DMPC formulation. The alternative formulation retains desirable features like the ability to perform energy saving, when demand does not exceed supply, and to effectively distribute energy without disproportionally harming any of the building users, when the system experiences a shortage of energy supply. Simulation and experimental results obtained in a solar energy research center located in Almería, Spain, are reported and discussed, showing promising results for the proposed control strategy.
Smart meters are being rolled out in large numbers throughout the world, with proponents claiming they are a critical step in the transition to a low-carbon economy. Yet there are significant unresolved negative reactions to smart meters, principally based on the concern that smart meters might be used to infer the private activities that occur within a dwelling. Though smart meter data is classified as personal data, and as such protected under existing data protection frameworks in the EU, there are relevant exceptions, notably where the data is required for legitimate applications associated with the performance of 'regulated duties'. This paper contributes to this debate by examining the data requirements for some of the proposed applications of smart meter data within the electricity supply industry, and investigates whether the use of personal data can be minimized or even avoided. The discussion includes system balancing, demand reduction, demand response and distribution network operation and planning, and indicates that, for most of these applications, the requirements for personal data can indeed be minimized. 'Privacy friendly' alternatives are discussed.
Demand side management will be a key component of future smart grid that can help reduce peak load and adapt elastic demand to fluctuating generations. In this paper, we consider households that operate different appliances including PHEVs and batteries and propose a demand response approach based on utility maximization. Each appliance provides a certain benefit depending on the pattern or volume of power it consumes. Each household wishes to optimally schedule its power consumption so as to maximize its individual net benefit subject to various consumption and power flow constraints. We show that there exist time-varying prices that can align individual optimality with social optimality, i.e., under such prices, when the households selfishly optimize their own benefits, they automatically also maximize the social welfare. The utility company can thus use dynamic pricing to coordinate demand responses to the benefit of the overall system. We propose a distributed algorithm for the utility company and the customers to jointly compute this optimal prices and demand schedules. Finally, we present simulation results that illustrate several interesting properties of the proposed scheme.
The rapidly growing world energy use has already raised concerns over supply difficulties, exhaustion of energy resources and heavy environmental impacts (ozone layer depletion, global warming, climate change, etc.). The global contribution from buildings towards energy consumption, both residential and commercial, has steadily increased reaching figures between 20% and 40% in developed countries, and has exceeded the other major sectors: industrial and transportation. Growth in population, increasing demand for building services and comfort levels, together with the rise in time spent inside buildings, assure the upward trend in energy demand will continue in the future. For this reason, energy efficiency in buildings is today a prime objective for energy policy at regional, national and international levels. Among building services, the growth in HVAC systems energy use is particularly significant (50% of building consumption and 20% of total consumption in the USA). This paper analyses available information concerning energy consumption in buildings, and particularly related to HVAC systems. Many questions arise: Is the necessary information available? Which are the main building types? What end uses should be considered in the breakdown? Comparisons between different countries are presented specially for commercial buildings. The case of offices is analysed in deeper detail.