Demand response (DR) is one of the integral mechanisms of today's smart grids. It enables consumer energy assets such as flexible loads, standby generators and storage systems to add value to the grid by providing cost-effective flexibility. With increasing renewable generation and impending electric vehicle deployment, there is a critical need for large volumes of reliable and responsive flexibility through DR. This poses a new challenge for the electricity sector. Smart grid development has resulted in the availability of large amounts of data from different physical segments of the grid such as generation, transmission, distribution and consumption. For instance, smart meter data carrying valuable information is increasingly available from the consumers. Parallel to this, the domain of data analytics and machine learning (ML) is making immense progress. Data-driven modelling based on ML algorithms offers new opportunities to utilise the smart grid data and address the DR challenge. The thesis demonstrates the use of data-driven models for enhancing DR from large consumers such as commercial and industrial (C&I) buildings. A reliable, computationally efficient, cost-effective and deployable data-driven model is developed for large consumer building load estimation. The selection of data pre-processing and model development methods are guided by these design criteria. Based on this model, DR operational tasks such as capacity scheduling, performance evaluation and reliable operation are demonstrated for consumer energy assets such as flexible loads, standby generators and storage systems. Case studies are designed based on the frameworks of ongoing DR programs in different electricity markets. In these contexts, data-driven modelling shows substantial improvement over the conventional models and promises more automation in DR operations. The thesis also conceptualises an emissions-based DR program based on emissions intensity data and consumer load flexibility to demonstrate the use of smart grid data in encouraging renewable energy consumption. Going forward, the thesis advocates data-informed thinking for utilising smart grid data towards solving problems faced by the electricity sector.

Energy consumption in the residential sector of Ontario is expected to grow by 15%, most of which is expected to be from electricity use, with an annual average growth rate of 0.9% between 2010 and 2020. With Ontario government’s Integrated Power System Plan (IPSP) recommending phasing out coal fired generators by 2014, the execution of Conservation and Demand Management and Demand Response programs can have significant impact on reducing power consumption and peak demand in the province. Electricity generation, especially from fossil fuel, contributes 18% of total green house gas (GHG) emissions in Ontario. With climate change effects being attributed to GHG emissions and environmental regulations, it is necessary to reduce GHG emissions from power generation sector. In this context, the current Energy Hub Management System project, of which the work presented here is a part, may lead to the reduction of electricity power demand and GHG emissions in Ontario. This thesis presents the validation of Energy Hub Management System (EHMS) residential sector model. Performances of individual appliances and the results obtained from various case-studies considering the EHMS model are compared with respect to a base case representing a typical residential customer. The case-studies are carefully developed to demonstrate the capability of the EHMS model to generate optimum operational schedules to minimize energy costs, energy consumption and emissions based on user defined constraints and preferences. Furthermore, a forecasting methodology based on single variable econometric time series is developed to estimate day-ahead CO2 emissions from Ontario’s power generation sector. The forecasted emissions profile is integrated into the EHMS model to optimize a residential customer’s contribution to CO2 emissions in Ontario.

This thesis develops a power line carrier(PLC) communication module using FSK modulation technology by integration of PLC chip, with various hardware circuits such as DSP, signal coupling and amplifier circuits, filter. The communication performance and conduction EMI tests and executed for the communication module developed. The PLC module is then applied for appliance control of commercial customers to fulfill the demand response function for energy conservation by reducing the summer peak loading. Besides sending the load control command from central station in the smart grid, the power consumption of various appliances can also be collected and transmitted back to the control station via two way communication with the PLC communication module. Finally, the broadband PLC (BPLC) is applied for the CCTV supervision in system to support asset management of distribution room to prevent the power equipment from steal. With the remote control of light brightness and CCTV lens with high data transmission rate provided, the communication performance of PLC can therefore be verified in this study.

Στόχος της παρούσας διπλωματικής εργασίας είναι να γίνει μια εκτίμηση και ρύθμιση τη λειτουργίας κάποιων συσκευών ενός «έξυπνου» σπιτιού σε ένα ενσωματωμένο σύστημα. Η λογική είναι να μπορεί ο χρήστης να δει ποια είναι η μέση κατανάλωση ισχύος μιας συσκευής και το αντίστοιχο χρηματικό κόστος που αναλογεί σε αυτήν, για κάποιο χρονικό διάστημα λειτουργίας της και υπολογισμού της βέλτιστης συμπεριφορά της συσκευής έτσι ώστε να επιτύχουμε την ελάχιστη δυνατή κατανάλωση. Τα φορτία τα οποία ελέγχθηκαν είναι ένα κλιματιστικό, ένα πλυντήριο και ένας θερμοσίφωνας. Η υλοποίηση έγινε σε ένα ενσωματωμένο σύστημα και δημιουργήθηκε και ένα περιβάλλον γραφικής απεικόνισης για επικοινωνία με τον χρήστη. / Simulation of the behavior of three home electrical devices with the use of demand response algorithms. Calculation of energy consumption of each device and money charge. Setting the behavior of each device to minimize total cost.

Energy consumption in the residential sector of Ontario is expected to grow by 15%, most of which is expected to be from electricity use, with an annual average growth rate of 0.9% between 2010 and 2020. With Ontario government’s Integrated Power System Plan (IPSP) recommending phasing out coal fired generators by 2014, the execution of Conservation and Demand Management and Demand Response programs can have significant impact on reducing power consumption and peak demand in the province. Electricity generation, especially from fossil fuel, contributes 18% of total green house gas (GHG) emissions in Ontario. With climate change effects being attributed to GHG emissions and environmental regulations, it is necessary to reduce GHG emissions from power generation sector. In this context, the current Energy Hub Management System project, of which the work presented here is a part, may lead to the reduction of electricity power demand and GHG emissions in Ontario. This thesis presents the validation of Energy Hub Management System (EHMS) residential sector model. Performances of individual appliances and the results obtained from various case-studies considering the EHMS model are compared with respect to a base case representing a typical residential customer. The case-studies are carefully developed to demonstrate the capability of the EHMS model to generate optimum operational schedules to minimize energy costs, energy consumption and emissions based on user defined constraints and preferences. Furthermore, a forecasting methodology based on single variable econometric time series is developed to estimate day-ahead CO2 emissions from Ontario’s power generation sector. The forecasted emissions profile is integrated into the EHMS model to optimize a residential customer’s contribution to CO2 emissions in Ontario.

By using IT-solutions, the flexibility on the demand side in the electrical systems could be increased. This is called demand response and is part of the larger concept called smart grids. Previous work in this area has concerned the utilization of demand response by grid owners. In this thesis the focus will instead be shifted towards the electrical companies that have balance responsibility, and how they could use demand response in order to make profits. By investigating electrical appliances in hourly measured households, the business value from decreasing electrical companies’ power imbalances has been quantified. By an iterative simulation scheme an optimal value was found to be 977 SEK/year and appliance. It could however be shown that the value became larger for energy inefficient households, and that such consumers’ participation in a demand response market would be prioritized ahead of other measures like isolating walls is rather unlikely. Thermal appliance whose load depend on the outdoor temperature are less valuable for demand response during the summer months, and the annual value would increase if less seasonally dependent appliances were used. Additionally, by increasing the market price amplitudes and the imbalance price volatility, it could be shown that the potential for such demand response markets is larger in e.g. the Netherlands and Germany.

Efforts towards a low carbon economy are challenging the electricity industry. On the supply-side, centralised carbon-intensive power plants are set to gradually decrease their contribution to the generation mix, whilst distributed renewable generation is to successively increase its share. On the demand-side, electricity use is expected to increase in the future due to the electrification of heating and transport. Moreover, the demand-side is to become more active allowing end-users to invest in generation and storage technologies, such as solar photovoltaics (PV) and home batteries. As a result, some network reinforcements might be needed and instrumentation at the users’ end is to be required, such as controllers and home energy management systems (HEMS). The electricity grid must balance supply and demand at all times in order to maintain technical constraints of frequency, voltage, and current; and this will become more challenging as a result of this transition. Failure to meet these constraints compromises the service and could damage the power grid assets and end-users’ appliances. Balancing generation, although responsive, is carbon-intensive and associated with inefficient asset utilisation, as these generators are mostly used during peak hours and sit idle the rest of the time. Furthermore, energy storage is a potential solution to assist the balancing problem in the presence of non-dispatchable low-carbon generators; however, it is substantially expensive to store energy in large amounts. Therefore, demand response (DR) has been envisioned as a complementary solution to increase the system’s resilience to weather-dependent, stochastic, and intermittent generation along with variable and temperature-correlated electric load. In the domestic setting, operational flexibility of some appliances, such as heaters and electric cars, can be coordinated amongst several households so as to help balance supply and demand, and reduce the need of balancing generators. Against this background, the electricity supply system requires new organisational paradigms that integrate DR effectively. Although some dynamic pricing schemes have been proposed to guide DR, such as time of use (ToU) and real-time pricing (RTP), it is still unclear how to control oscillatory massive responses (e.g., large fleet of electric cars simultaneously responding to a favourable price). Hence, this thesis proposes an alternative approach in which households proactively submit DR offers that express their preferences to their respective retailer in exchange for a discount. This research develops a computational model of domestic electricity use, and simulates appliances with operational flexibility in order to evaluate the effects and benefits of DR for both retailers and households. It provides a representation for this flexibility so that it can be integrated into specific DR offers. Retailers and households are modelled as computational agents. Furthermore, two market-based mechanisms are proposed to determine the allocation of DR offers. More specifically, a one-sided Vickrey-Clarke-Groves (VCG)-based mechanism and penalty schemes were designed for electricity retailers to coordinate their customers’ DR efforts so as to ameliorate the imbalance of their trading schedules. Similarly, a two-sided McAfee-based mechanism was designed to integrate DR offers into a multi-retailer setting in order to reduce zonal imbalances. A suitable method was developed to construct DR block offers that could be traded amongst retailers. Both mechanisms are dominant-strategy incentive-compatible and trade off a small amount of economic efficiency in order to maintain individual rationality, truthful reporting, weak budget balance and tractable computation. Moreover, privacy preserving is achieved by including computational agents from the independent system operator (ISO) as intermediaries between each retailer and its domestic customers, and amongst retailers. The theoretical properties of these mechanisms were proved using worst-case analysis, and their economic effects were evaluated in simulations based on data from a survey of UK household electricity use. In addition, forecasting methods were assessed on the end-users’ side in order to make better DR offers and avoid penalties. The results show that, under reasonable assumptions, the proposed coordination mechanisms achieve significant savings for both end-users and retailers, as they reduce the required amount of expensive balancing generation.

Due to the increasing integration of renewable energy generations, the overvoltage and overload issues in transmission networks have become more significant, and they may occur at various locations. To mitigate the overvoltage issues, traditional solutions which often consider the installation of reactive power compensators such as shunt reactors, SVC, STATCOM may not be cost-effective. To mitigate the overload issues, traditional methods using direct or price-based demand control will affect customers’ electrical experience in that they are inconvenienced greatly. This thesis discusses the flexible tap changing techniques that utilise existing parallel transformers in distribution networks to provide reactive power absorption and demand response services for transmission systems. Among them, the tap stagger technique operates parallel transformers in small different tap positions, i.e. staggered taps, to result in more reactive power absorption from upstream networks. In addition, the tap changing technique changes voltages in the range of statutory limits through the adjustment of tap positions in order to change network demands without directly affecting customers. The aggregated reactive power absorption or demand response from many pairs of parallel transformers in distribution networks could be sufficient to provide VAr or demand support to transmission networks. Network capability studies have been carried out in OpenDSS simulation software to investigate the VAr absorption capability by using tap staggering technique and the demand reduction capability by using tap changing technique. The studies are based on two UK HV distribution networks (132-33kV) with 11 and 28 primary substations (33/11 or 6.6 kV) respectively, and the techniques are applied to parallel transformers in primary substations. Based on the results of the two networks, the capabilities of the whole ENW and the UK distribution networks have been estimated respectively by using linear estimation method. In addition, the VAr absorption capability of the tap stagger technique has been validated by using site trial data. The results show an average VAr absorption capability of 0.89MVAr for a primary substation, 315MVAr for ENW networks and about 2500MVAr for the UK at stagger level 4 and show an average demand reduction capability of 3.1% of the original demand at tap down level 3. The results of capability studies together with the validations results confirm that the flexible tap changing techniques are able to provide transmission networks with effective VAr support and demand response services. To assess network VAr absorption and demand response capability more precisely, this thesis also proposes an online load profile estimation method to estimate the load profiles of the network more accurately if not all substations in the network are monitored. The method uses Peak Load Share values, Euclidean Distance, and some load measurements to estimate load profiles. The method has been validated and compared with a traditional aggregation-based method. The results show an average estimation error of 13% ~ 23% in different conditions using the proposed method, and show an average estimation error reduction from about 47% (using the traditional method) to about 13% (using the proposed method). The results indicate that the developed method has a considerable improvement on the accuracy of load profile estimation.

This dissertation presents the design, analysis, and validation of a hierarchical transactive control system that engages demand response resources to enhance the integration of renewable electricity generation resources. This control system joins energy, capacity and regulation markets together in a unified homeostatic and economically efficient electricity operation that increases total surplus while improving reliability and decreasing carbon emissions from fossil-based generation resources. The work encompasses: (1) the derivation of a short-term demand response model suitable for transactive control systems and its validation with field demonstration data; (2) an aggregate load model that enables effective control of large populations of thermal loads using a new type of thermostat (discrete time with zero deadband); (3) a methodology for optimally controlling response to frequency deviations while tracking schedule area exports in areas that have high penetration of both intermittent renewable resources and fast-acting demand response; and (4) the development of a system-wide (continental interconnection) scale strategy for optimal power trajectory and resource dispatch based on a shift from primarily energy cost-based approach to a primarily ramping cost-based one. The results show that multi-layer transactive control systems can be constructed, will enhance renewable resource utilization, and will operate in a coordinated manner with bulk power systems that include both regions with and without organized power markets. Estimates of Western Electric Coordinating Council (WECC) system cost savings under target renewable energy generation levels resulting from the proposed system exceed US$150B annually by the year 2024, when compared to the existing control system. / Graduate

The climate change is driving a change in technology and promotes intermittent electricity; solar and wind, and also promotes new technology such as electrical vehicles. The increased share of intermittent  power and changed patterns of using power causes large strain on the powergrids during critical hours of the year. The system The Eergimarknadsinspektionen as well as the European transmission system operators are therefore requesting that electricity users adapt their power consumption to when power is generated. This is rather opposite to the present situation where the TSO’s respond to the customers demand by increasing their power generation. This new change of customers adapting to the current power available is called Demand response (DR). The thesis investigates drivers, barriers and potential for demand response within the engineering industry. It is based on interviews with representatives from enginering industries, system operators as well as a case study on Volvo Group Trucks Operations Powertrain Production in Köping. The potential is also determined through a simulation carried out in collaboration with Johan Norberg, a masterstudent at the Royal Technical Highschool. The conclusion states that it is possible for Volvo Pwertrain to participate in DR events, however the economical compensation identified in this thesis is not enough.