Electric Vehicles (EVs) are active loads as they increase the demand for electricity and introduce several challenges to electrical distribution feeders during charging. Demand Response (DR) or performing load control in commercial buildings along with the deployment of solar photovoltaic (PV) and ice storage systems at the building level can improve the efficiency of electricity grids and mitigate expensive peak demand/energy charges for buildings. This research aims to provide such a solution to make EV penetration transparent to the grid. Firstly, this research contributes to the development of an integrated control of major loads, i.e., Heating Ventilation and Air Conditioning (HVAC), lighting and plug loads while maintaining occupant environmental preferences in small- and medium-sized commercial buildings which are an untapped DR resource. Secondly, this research contributes to improvement in functionalities of EnergyPlus by incorporating a 1-minute resolution data set at the individual plug load level. The research evaluates total building power consumption performance taking into account interactions among lighting, plug load, HVAC and control systems in a realistic manner. Third, this research presents a model to study integrated control of PV and ice storage on improving building operation in demand responsive buildings. The research presents the impact of deploying various combinations of PV and ice storage to generate additional benefits, including clean energy generation from PV and valley filling from ice storage, in commercial buildings. Fourth, this research presents a coordinated load control strategy, among participating commercial buildings in a distribution feeder to optimally control buildings' major loads without sacrificing occupant comfort and ice storage discharge, along with strategically deployed PV to absorb EV penetration. Demand responsive commercial building load profiles and field recorded EV charging profiles have been added to a real world distribution circuit to analyze the effects of EV penetration, together with real-world PV output profiles. Instead of focusing on individual building's economic benefits, the developed approach considers both technical and economic benefits of the whole distribution feeder, including maintaining distribution-level load factor within acceptable ranges and reducing feeder losses. / Ph. D.

An electric power system is a complex network consisting of a large number of power generators and consumers interconnected by transmission and distribution lines. One remarkable thing about the electric grid is that there has to be a continuous balance between the amount of electricity generated and consumed at all times. Maintaining this balance is critical for the stable operation of the grid and this task is achieved in the long term, short term and real-time by operating a three-tier wholesale electricity market consisting of the capacity market, the energy market and the ancillary services market respectively. For a demand resource to participate in the energy and the capacity markets, it needs to be able to reduce the power consumption on-demand, whereas to participate in the ancillary services market, the power consumption of the demand resource needs to be varied continuously following the regulation signal sent by the grid operator. This act of changing the demand to help maintain energy balance is called demand response (DR). The dissertation presents novel algorithms and tools to enable residential buildings to participate as demand resources on such markets to provide DR. Residential sector consumes 37% of the total U.S. electricity consumption and a recent consumer survey showed that 88% of consumers are either eager or supportive of advanced technologies for energy efficiency, including demand response. This indicates that residential sector is a very good target for DR. Two broad solutions for residential DR are presented. The first is a set of efficient algorithms that intelligently controls the customers' heating, ventilating and air conditioning (HVAC) devices for providing DR services to the grid. The second solution is an extensible residential demand response simulation framework that can help evaluate and experiment with different residential demand response algorithms. One of the algorithms presented in this dissertation is to reduce the aggregated demand of a set of HVACs during a DR event while respecting the customers' comfort requirements. The algorithm is shown to be efficient, simple to implement and is proven to be optimal. The second algorithm helps provide the regulation DR while honoring customer comfort requirements. The algorithm is efficient, simple to implement and is shown to perform well in a range of real-world situations. A case study is presented estimating the monetary benefit that can be obtained by implementing the algorithm in a cluster of 100 typical homes and shows promising result. Finally, the dissertation presents the design of a python-based object-oriented residential DR simulation framework which is easy to extend as needed. The framework supports simulation of thermal dynamics of a residential building and supports house hold appliances such as HVAC, water heater, clothes washer/dryer and dish washer. A case study showing the application of the simulation framework for various DR implementation is presented, which shows that the simulation framework performs well and can be a useful tool for future research in residential DR. / PHD / The total power generation and consumption has to always match in the electric grid. When there is a mismatch because the generation is less than the load, the match can be restored either by increasing the generation or by decreasing the load. Often, during system stress conditions, it is cheaper to decrease certain loads than to increase generation, and this method of achieving power balance is called demand response (DR). Residential sector consumes 37% of the total U.S. electricity consumption and is largely unexplored for demand response purpose, so the focus of the dissertation is on providing solutions to enable residential houses to provide demand response services. This dissertation presents two broad solutions. The first is a set of efficient algorithms that intelligently controls the customers’ heating, ventilating and air conditioning (HVAC) devices for providing DR services to the grid while keeping their comfort in mind. The second solution is a simulation software that can help evaluate and experiment with different residential demand response algorithms. The first algorithm is for reducing the collective power consumption of an aggregation of residential HVAC, whereas the second algorithm is for making the collective power follow a signal sent by the grid operators. It is shown that the algorithms presented can intelligently control the HVAC devices such that DR services can be provided to the grid while ensuring that the temperatures of the houses remain within comfortable range. The algorithms can enable demand response service providers to tap into the residential demand response market and earn revenue, while the simulation software can be valuable for future research in this area. The simulation software is simple to use and is designed with extensibility in mind, so adding new features is easy. The software is shown to work well for studying residential building control for demand response purpose and can be a useful tool for future research in residential DR.

Electrical power systems consist of a large number of power generators connected to consumers through a complex system of transmission and distribution lines. Within the electric grid, a continuous balance between generation and consumption of electricity must be maintained., ensuring stable operation of the grid. In recent decades due to increasing electricity demand, there is an increased likelihood of electrical power systems experiencing stress conditions. These conditions lead to a limited supply and cascading failures throughout the grid that could lead to wide area outages. Demand Response (DR) is a method involving the curtailment of loads during critical peak load hours, that restores that balance between demand and supply of electricity. In order to implement DR and ensure efficient energy operation of buildings, detailed energy monitoring is essential. This information can then be used for energy management, by monitoring the power consumption of devices and giving users detailed feedback at an individual device level. Based on the data from the Energy Information Administration (EIA), approximately half of all commercial buildings in the U.S. are 5,000 square feet or smaller in size, whereas the majority of the rest is made up of medium-sized commercial buildings ranging in size between 5,001 and 50,000 square feet. Given that these medium-size buildings account for a large portion of the total energy demand, these buildings are an ideal target for participating in DR. In this dissertation, two broad solutions for commercial building DR have been presented. The first is a load disaggregation technique to disaggregate the power of individual HVACs using machine learning classification techniques, where a single power meter is used to collect aggregated HVAC power data of a building. This method is then tested over a number of case studies, from which it is found that the aggregated power data can be disaggregated to accurately predict the power consumption and state of activity of individual HVAC loads. The second work focuses on a DR algorithm involving the determination of an optimal bid price for double auctioning between the user and the electric utility, in addition to a load scheduling algorithm that controls single floor HVAC and lighting loads in a commercial building, considering user preferences and load priorities. A number of case studies are carried out, from which it is observed that the algorithm can effectively control loads within a given demand limit, while efficiently maintaining user preferences for a number of different load configurations and scenarios. Therefore, the major contributions of this work include- A novel HVAC power disaggregation technique using machine learning methods, and also a DR algorithm for HVAC and lighting load control, incorporating user preferences and load priorities based on a double-auction approach. / Doctor of Philosophy / Electrical power systems consist of a large number of power generators connected to consumers through a complex system of transmission and distribution lines. Within the electric grid, a continuous balance between generation and consumption of electricity must be maintained., ensuring stable operation of the grid. When electricity demand is high, Demand Response (DR) is a method that can be used to reduce user loads, restoring the balance between demand and supply of electricity. Based on data from the Energy Information Administration (EIA), half of all commercial buildings in the US measure 5,000 square feet or smaller in size, whereas the majority of the other half is made up of medium-sized commercial buildings measuring in at between 5,001 to 50,000 square feet. This makes these commercial buildings an ideal target for participating in DR. In this dissertation, two broad solutions for commercial building DR have been presented. The first is a load disaggregation technique, where power consumption and activity of individual HVACs can be obtained, using a single power meter. The second work focuses on a DR algorithm, that controls single floor HVAC and lighting loads in a commercial building, based on a user generated bid price for electricity, user preferences and load priorities, when electricity demand is at its peak.

The demand for electricity of a particular location can vary significantly based on season, ambient temperature, time of the day etc. High demand can result in very high wholesale price of electricity. The reason for this is very short operating duration of peaking power plants which require large capital investments to establish. Those power plants remain idle for most of the time of a year except for some peak demand periods during hot summer days. This process is inherently inefficient but it is necessary to meet the uninterrupted power supply criterion. With the advantage of new technologies, demand response can be a preferable alternative, where peak reduction can be obtained during the short durations of peak demand by controlling loads. Some controllable loads are with thermal inertia and some loads are deferrable for a short duration without making any significant impact on users' lifestyle and comfort. Demand response can help to attain supply - demand balance without completely depending on expensive peaking power plants. In this research work, an incentive-based model is considered to determine the potential of peak demand reduction due to the participation of residential customers in a demand response program. Electric water heating and air-conditioning are two largest residential loads. In this work, hot water preheating and air-conditioning pre-cooling techniques are investigated with the help of developed mathematical models to find out demand response potentials of those loads. The developed water heater model is validated by comparing results of two test-case simulations with the expected outcomes. Additional energy loss possibility associated with water preheating is also investigated using the developed energy loss model. The preheating temperature set-point is mathematically determined to obtain maximum demand reduction by keeping thermal loss to a minimal level. Case studies are performed for 15 summer days to investigate the demand response potential of water preheating. Similarly, demand response potential associated with pre-cooling operation of air-conditioning is also investigated with the help of the developed mathematical model. The required temperature set-point modification is determined mathematically and validated with the help of known outdoor temperature profiles. Case studies are performed for 15 summer days to demonstrate effectiveness of this procedure. On the other hand, total load and demand response potential of a single house is usually too small to participate in an incentive-based demand response program. Thus, the scope of combining several houses together under a single platform is also investigated in this work. Monte Carlo procedure-based simulations are performed to get an insight about the best and the worst case demand response outcomes of a cluster of houses. In case of electrical water heater control, aggregate demand response potential of 25 houses is determined. Similarly, in case of air-conditioning control (pre-cooling), approximate values of maximum, minimum and mean demand reduction amounts are determined for a cluster of 25 houses. Expected increase in indoor temperature of a house is calculated. Afterwards, the air-conditioning demand scheduling algorithm is developed to keep aggregate air-conditioning power demand to a minimal level during a demand response event. Simulation results are provided to demonstrate the effectiveness of the proposed algorithm. / Master of Science

Along with the growth of electricity demand and the penetration of intermittent renewable energy sources, electric power distribution networks will face more and more stress conditions, especially as electric vehicles (EVs) take a greater share in the personal automobile market. This may cause potential transformer overloads, feeder congestions, and undue circuit failures. Demand response (DR) is gaining attention as it can potentially relieve system stress conditions through load management. DR can possibly defer or avoid construction of large-scale power generation and transmission infrastructures by improving the electric utility load factor. This dissertation proposes to develop a planning tool for electric utilities that can provide an insight into the implementation of demand response at the end-user level. The proposed planning tool comprises control algorithms and a simulation platform that are designed to intelligently manage end-use loads to make the EV penetration transparent to an electric power distribution network. The proposed planning tool computes the demand response amount necessary at the circuit/substation level to alleviate the stress condition due to the penetration of EVs. Then, the demand response amount is allocated to the end-user as a basis for appliance scheduling and control. To accomplish the dissertation objective, electrical loads of both residential and commercial customers, as well as EV fleets, are modeled, validated, and aggregated with their control algorithms proposed at the appliance level. A multi-layer demand response model is developed that takes into account both concerns from utilities for load reduction and concerns from consumers for convenience and privacy. An analytic hierarchy process (AHP)-based approach is put forward taking into consideration opinions from all stakeholders in order to determine the priority and importance of various consumer groups. The proposed demand response strategy takes into consideration dynamic priorities of the load based on the consumers' real-time needs. Consumer comfort indices are introduced to measure the impact of demand response on consumers' life style. The proposed indices can provide electric utilities a better estimation of the customer acceptance of a DR program, and the capability of a distribution circuit to accommodate EV penetration. Research findings from this work indicate that the proposed demand response strategy can fulfill the task of peak demand reduction with different EV penetration levels while maintaining consumer comfort levels. The study shows that the higher number of EVs in the distribution circuit will result in the higher DR impacts on consumers' comfort. This indicates that when EV numbers exceed a certain threshold in an area, other measures besides demand response will have to be taken into account to tackle the peak demand growth. The proposed planning tool is expected to provide an insight into the implementation of demand response at the end-user level. It can be used to estimate demand response potentials and the benefit of implementing demand response at different DR penetration levels within a distribution circuit. The planning tool can be used by a utility to design proper incentives and encourage consumers to participate in DR programs. At the same time, the simulation results will give a better understanding of the DR impact on scheduling of electric appliances. / Ph. D.

This master thesis evaluates the incentives of a newly implemented market controlled network tariff by analyzing consumption data and constructing a time-differentiated debiting model. The tariff was implemented by Sandviken Energi Elnät AB and the thesis evaluates its customers consumption data compared to data provided by Sundsvall Elnät AB. The differences in data is evaluated by statistical tests of Students t-test, Bayesian t-test and χ2-test with the result that no statistically significant change in user pattern can be found and thus an elucidation of the incentives must be made in the form of a hourly time-differentiated debating model. The thesis also evaluates the cost incentives of the model compared to spot prices where it is shown that the tariff model can benefit much from the spot prices hourly incentives in its hourly time-differentiated model. Five time-differentiated models were constructed and presented where three uses a color coding scheme. The conclusion is that a color coded time-differentiated tariff should give the costumers clear and cost-effective incentives.

Die Verwendung elektrischer Energie ist ein essenzieller Bestandteil industrieller Prozesse. Aufgrund der aktuellen Bemühungen für eine Energiewende hin zu regenerativen Energien ist es sehr wahrscheinlich, dass die industrielle Bedeutung des elektrischen Stromes weiter zunehmen wird. Damit dabei die Ziele der Roadmap 2050, eine Reduzierung des CO2-Ausstoßes um 80 % bezogen auf 1990, erreichbar bleiben, bedarf es der unausweichlichen Etablierung erneuerbarer Energien. Dies ist kostenintensiv und aufgrund der ungleichmäßigen Energiebereitstellung nicht ohne Probleme für die aufgebauten Netzstrukturen. Der flexible Leistungsbezug (Demand Response) wird aufgrund seines netzdienlichen Charakters als ein Schlüssel zur Netzstabilisierung und direkten Energiekostenreduzierung gesehen. Jedoch trägt die energieintensive Papierindustrie mit ihren Papiermaschinen bislang keinen bedeutenden Teil zu diesem Ansatz bei und profitiert demnach auch nicht von den finanziellen Vorteilen. Eine Ursache dafür ist, dass dieser Branche auf Grundlage der bisherigen Be-wertungsmethoden kaum wirtschaftliche Potentiale für einen flexiblen Lastbezug zugespro-chen werden. Ein wesentlicher Teil der vorliegenden Arbeit widmet sich der Entwicklung eigner, den Be-dürfnissen der Papierindustrie angepassten, Erhebungs-, Bewertungs- sowie angeschlosse-nen Vermarktungsmethoden für eine Energiekostenreduzierung auf der Grundlage flexibler Lasten. Für die Kostenoptimierung stehen unterschiedliche Möglichkeiten, wie die in der vor-liegenden Arbeit betrachtete Regelleistungsvermarktung, zur Verfügung. Die Ergebnisse eines sortenspezifischen Analyseansatzes von Prozess- und Energieein-satzkennzahlen zeigen, dass von der Papierindustrie ein größeres Mitwirken am Regelleis-tungsmarkt zu erwarten sein kann, als es bisher angenommen wurde. Die erkannten Poten-tiale zur Lastflexibilisierung sind jedoch nicht jederzeit verfügbar. Damit diese Verfügbarkeit nicht überschritten wird, müssen die Mechanismen, die zum Regellastabruf führen, verstan-den und deren Einfluss auf einen Lastabruf genutzt werden. Zu diesem Zweck wurde im Zuge der Arbeit die Grenzlast eingeführt. Die Grenzlast kennzeichnet die Regellasthöhe, welche aus statistischer Sicht nicht öfter abgerufen wird, als es die Verfügbarkeit zulässt. Es wird belegt, dass auch von der Papierindustrie eine Beteiligung am Regelenergiemarkt möglich ist und die Energiekosten auf diese Weise reduzierbar wären, ohne den Fokus auf das Kerngeschäft zu verlieren.:I. Abbildungsverzeichnis II. Tabellenverzeichnis III. Formelverzeichnis IV. Abkürzungen, Formelzeichen V. Thesenübersicht 1 Einleitung und Motivation 1.1 Zielstellung und Aufbau der Arbeit 1.1.1 Ziele der Untersuchungen 1.1.2 Abgrenzung zu verfügbaren Software-Lösungen 1.2 Energieoptimierung – Sichtweisen und Definitionen 1.3 Energiepolitik und umweltpolitische Forderungen 1.3.1 Roadmap 2050 1.3.2 Zieldreieck 1.3.3 Flexible Lasten und der zukünftige Energiemarkt 2 Theoretische Grundlagen 2.1 Key Perfomance Indicator - Schlüsselfaktoren 2.2 Kennzahlen der Papierindustrie 2.3 Energiesystem in Deutschland 2.3.1 Energiepreise – Preisbildung 2.4 Netzregulierung – Regelleistung 2.5 Charakterisierung von Regelleistungsarten 2.5.1 Datenaufbereitung des Regelleistungseinsatzes 2.5.2 Minutenreserve 2.5.3 Sekundärreserve 2.6 Demand Response 2.6.1 Demand Response - Definition 2.6.2 Demand Response – Speicher 2.6.3 Finanzielle Auswirkungen von Demand Response 3 Methodik – Entwicklung und Durchführung 3.1 Ableitung konkreter Arbeitsaufgaben 3.2 Erhebung sortenspezifischer Kennzahlen 3.2.1 Sortenspezifische Kennwertberechnung 3.2.2 For-Schleife 3.2.3 Anwendungssoftware 3.2.4 Überführung produktspezifischer Kennzahlen in den Produktionsplan 3.3 Potentialerhebung flexibler Lasten 3.3.1 Potentialermittlung nach Klobasa 3.3.2 Diskussion der Klobasa Methode im Kontext weiterer Methoden 3.4 Analyse der Auktionsergebnisse von Regelleistung 3.5 Ermittlung und Einflussnahme auf die Abrufdauer von Regelleistung 3.5.1 Grenzlastprognose 3.5.2 Entwicklung der Preisstruktur zur Energiekostenoptimierung 3.6 Eignungsbewertung der ermittelten Regelleistungspotentiale 4 Anwendung grundlegender Erkenntnisse und Methoden 4.1 Reservelastpotential durch Änderung des Dampfbezuges 4.1.1 Ermittlung der Potentialhöhe 4.1.2 Qualitätsbewertung der potentiellen Reserveleistung 4.1.3 Ermittlung der Potentialverfügbarkeit 4.1.4 Herleitung und Bewertung des Arbeitspreises 4.1.5 Herleitung und Bewertung des Leistungspreises 4.1.6 Bestimmung der Energiekostenoptimierung 4.2 Reservelastpotential durch Änderung der Antriebslast - Ausblick 4.2.1 Qualitätsbewertung – sortenspezifische Betrachtung der Antriebslast 5 Effizienzsteigerung durch sortenspezifische Kennwerte 5.1 Energieoptimierungssystem 5.1.1 Zielwerterhebung im Energie Optimierungs System (EOS) 5.1.2 Funktionsweise des EOS 6 Zusammenfassung VI. Literaturverzeichnis VII. Anhang

District heating plays a major role in the Swedish energy system. It is deemed a renewable energy source and is the main provider for multi-family dwellings with 90 %. Although the district heating fuel mix consists of majority renewables, a share of 5 % is provided from fossil fuels. To reduce fossil fuel usage and eradicate CO2-emissions from the district heating system new solutions are sought after. In this project, the potential for shortterm thermal energy storage in buildings is investigated. This concept is referred to as customer flexibility. Demand flexibility is created in the district heating system (DHS) by varying the indoor temperature in 50 multi-family dwellings with maximum 1◦C, without jeopardizing the thermal comfort for the tenants. The flexible load makes it possible to store energy shortterm in the building’ envelope. Consequently, heat load curves are evened in production. This leads to a reduction of the peak load in the DHS. Peaks are associated with high costs and environmental impact. Therefore, the potential benefits of customer flexibility are reduced peak production, fuel costs, and CO2-emissions, depending on the fuel mix in the DHS. The project objective is to examine the techno-economic potential of customer flexibility in a specific DHS. The case study is made in a DHS owned by the company Vattenfall, located in the Stockholm area. To evaluate the potential benefits of implementing the concept, seven key performance indicators are chosen. They are peak power, peak fuel usage, produced volume, total fuel cost, fuel cost per produced MWh, climate footprint, and primary energy. Moreover, an in-house optimization model is used to simulate multiple scenarios of the district heating DHS. Different sets of assumptions about the available flexibility in the DHS and the thermal characteristics of the buildings are made. Customer flexibility is modeled as virtual heat storage that can be charged up or down depending on the speed and size of the available storage at a specific outdoor temperature. Simulation results give a maximum peak power reduction of 10.9 % and annual fuel cost reduction between 0.9-3.6 % depending on the scenario. The results found are comparable to values found in similar studies. However, the environmental key performance indicators generated an increase in CO2-emissions and primary energy compared to the baseline scenarios. The result would have looked different if fossil fuels were used in peak production instead of biofuels. The master thesis also aimed to validate assumptions and parameters made in the input data to the optimization model. This was achieved by using results attained from a pilot in the specific DHS. Therefore results generated from the simulations are deemed accurate and confirm that customer flexibility leads to reduced peak production and DHS optimization. / Se filen

Variable generator output levels from renewable energies is an important technical obstacle to the transition from fossil fuels to renewable resources. Super grids and smart grids are among the most effective solutions to mitigate generation variability. In a super grid, electric utilities within an interconnected system can share generation and reserve units so that they can produce electricity at a lower overall cost. Smart grids, in particular demand response programs, enable flexible loads such as plug-in electric vehicles and HVAC systems to consume electricity preferntially in a grid-friendly way that assists the grid operator to maintain the power balance. These solutions, in conjunction with energy storage systems, can facilitate renewable integration. This study aims to provide an understanding of the achievable benefits from integrating demand response into wholesale and retail electricity markets, in particular in the presence of significant amounts of variable generation. Among the options for control methods for demand response, market-based approaches provide a relatively efficient use of load flexibility, without restricting consumers' autonomy or invading their privacy. In this regard, a model of demand response integration into bulk electric grids is presented to study the interaction between variable renewables and demand response in the double auction environment, on an hourly basis. The cost benefit analysis shows that there exists an upper limit of renewable integration, and that additional solutions such as super grids and/or energy storage systems are required to go beyond this threshold. The idea of operating an interconnection in an unified (centralized) manner is also explored. The traditional approach to the unit commitment problem is to determine the dispatch schedule of generation units to minimize the operation cost. However, in the presence of price-sensitive loads (market-based demand response), the maximization of economic surplus is a preferred objective to the minimization of cost. Accordingly, a surplus-maximizing hour-ahead scheduling problem is formulated, and is then tested on a system that represents a 20-area reduced model of the North America Western Interconnection for the planning year 2024. The simulation results show that the proposed scheduling method reduces the total operational costs substantially, taking advantage of renewable generation diversity. The value of demand response is more pronounced when ancillary services (e.g. real-time power balancing and voltage/frequency regulation) are also included along with basic temporal load shifting. Relating to this, a smart charging strategy for plug-in electric vehicles is developed that enables them to participate in a 5-minute retail electricity market. The cost reduction associated with implementation of this charging strategy is compared to uncontrolled charging. In addition, an optimal operation method for thermostatically controlled loads is developed that reduces energy costs and prevents grid congestion, while maintaining the room temperature in the comfort range set by the consumer. The proposed model also includes loads in the energy imbalance market. The simulation results show that market-based demand response can contribute to a significant cost saving at the sub-hourly level (e.g. HVAC optimal operation), but not at the super-hourly level. Therefore, we conclude that demand response programs and super grids are complementary approaches to overcoming renewable generation variation across a range of temporal and spatial scales. / Graduate / 0791 / sahandbehboodi@gmail.com

The implementation of a low carbon electricity system within the GB requires a significant change to the generation mix with an increasing role for renewable generation. Much of this generation will be intermittent. To date system balancing has largely relied on predicting demand and ensuring provision. With substantial intermittency, continuation of this paradigm necessitates significant investment in peaking plant and/or storage. However, some of this investment can be avoided by harnessing the flexibility inherent in many electrical loads. Despite the attractiveness of such services, we do not see their large-scale implementation. The aim of this thesis is to consider why. A historical analysis reveals that both nationalisation and subsequent privatisation provide precedents for significant structural change as the integration of large-scale flexible demand might require. The need for political will is identified as a crucial enabling factor. Without an ideological driver, however, a perception of economic and/or technological risk can preclude the implementation of supportive policy. This perception is addressed through demonstration. An effective demonstration must show the ability to aggregate many small loads in a coordinated manner. A genetic algorithm that provides this core dispatch and optimisation capability is presented. This algorithm is shown to be effective in aggregating many small loads to provide a net effect that can be used as a balancing service and to do so in an optimal way considering both cost and reliability. Having demonstrated feasibility appropriate incentives must be created. An initial outline for a framework based on SysML is presented that can be used to identify where structural barriers to implementation are present to aid the design of appropriate policy incentives.