Participation of distributed loads in power markets that co-optimize energy and reserves

Thesis (Ph.D.)--Boston UniversityAs the integration of Renewable Generation into today's Power Systems is progressing rapidly, capacity reserve requirements needed to compensate for the intermittency of renewable generation is increasing equally rapidly. A major objective of this thesis is to promote the affordability of incremental reserves by enabling loads to provide them through demand response. Regulation Service (RS) reserves, a critical type of bi-directional Capacity Reserves, are provided today by expensive and environmentally unfriendly centralized fossil fuel generators. In contrast, we investigate the provision of low-cost RS reserves by the demand-side. This is a challenging undertaking since loads must first promise reserves in the Hour Ahead Markets, and then be capable of responding to the dynamic ISO signals by adjusting their consumption effectively and efficiently. To this end, we use Stochastic Control, Optimization Theory, and Approximate Dynamic Programming to develop a decision support framework that assists Smart Neighborhood Operators or Smart Building Operators (SNOs/SBOs) to become demand-side-providers of RS reserve. We first address the SNO/SBO short time scale operational task of responding to the Independent System Operator's (ISO) dynamic RS requests. We start by developing a model-based Markovian decision problem that trades off ISO RS tracking against demand response related utility loss. Starting with a model based approach we obtain near optimal operational policies through a novel approximate policy iteration technique and an actor critic approach which is robust to partial knowledge of the underlying system dynamics. We then abandon the model based terrain and solve the dynamic operational problem through reinforcement learning that is capable of modeling a population of duty cycle appliances with realistic thermodynamics. We finally propose a smart thermostat design and develop an adaptive control policy that can drive the smart thermostat effectively. The latter approach is particularly suited for systems whose dynamics and dynamically changing consumer preferences are not known or observed beyond the total power consumption. We then address the SNO/SBO task of bidding RS reserves to the hour ahead market. This task determines the maximal RS reserves that the SNO/SBO can promise based on information available at the beginning of an hour, so as to maximize the associated hour-ahead revenues minus the expected average operating cost that will be incurred during the operational task to follow. To accomplish this task, we (i) develop probabilistic constraints that model the feasible maximum reserves which can be offered to the market without exceeding the SNO/SBO's ability to later track the unanticipated dynamic ISO RS signal, and (ii) calibrate a describing function that approximates the average operational cost as a function of the maximal reserves that can be feasibly offered in the day ahead market. The above is made possible by statistical analysis of the controlled system's stochastic dynamics and properties of the optimal dynamic policies that we derive. The contribution of the thesis is twofold: The solution of a difficult stochastic control problem that is crucial for effective demand-response-based provision of regulation service, and, the characterization of key properties of the stochastic control problem solution, which allow its integration into the hour-ahead market bidding problem

Demand response from the control of aggregated inverter air conditioners

Inverter air conditioners (ACs) account for a large proportion of air conditioning loads in many countries and, thus, contribute significantly to the peak loads in these areas, especially in summer. On the other hand, as an important category of thermostatically controlled load with thermal energy storage capability, inverter ACs also have the potential to provide considerable flexibility for electric power systems that are faced with increasing challenges posed by high penetration of renewable power generation. This paper focuses on the demand response from the control of the aggregated inverter ACs for load reduction. A virtual energy storage system (VESS) model that encapsulates the room with an inverter AC was established based on the electric model of an inverter AC and the thermodynamic model of a room. Based on the VESS model, a virtual state of charge (VSOC) priority-based load reduction control method with temperature holding and linear recovery strategies was proposed. The VSOC priority based control was designed to decrease the negative impact of load reduction on customers’ thermal comfort from the perspective of the whole AC population. The temperature holding strategy was designed to reduce the electric power of an AC while ensuring that the indoor temperature is always below the allowable limit. The linear recover strategy was proposed to reduce the load rebound after load reduction. Four cases were studied regarding the operation and load reduction of the 100 inverter ACs, and the simulation results verified the models established and the effectiveness and advantages of the proposed load reduction control method

Enhanced frequency response from industrial heating loads for electric power systems

Increasing penetration of renewable generation results in lower inertia of electric power systems. To maintain the system frequency, system operators have been designing innovative frequency response products. Enhanced Frequency Response (EFR) newly introduced in the UK is an example with higher technical requirements and customized specifications for assets with energy storage capability. In this paper, a method was proposed to estimate the EFR capacity of a population of industrial heating loads, bitumen tanks, and a decentralized control scheme was devised to enable them to deliver EFR. Case study was conducted using real UK frequency data and practical tank parameters. Results showed that bitumen tanks delivered high-quality service when providing service-1-type EFR, but underperformed for service-2-type EFR with much narrower deadband. Bitumen tanks performed well in both high and low frequency scenarios, and had better performance with significantly larger numbers of tanks or in months with higher power system inertia

Modeling and Aggregation of Thermostatically Controlled Loads for Participation in Frequency and Voltage Control of a Power System

L’opération d’un réseau électrique est un acte de balancement puisque l’équilibre entre la production et la consommation doit être maintenue en temps réel. La fréquence et la tension sont des indicateurs précis des déséquilibres et doivent par conséquent être maintenues dans les limites autorisées pour un fonctionnement stable des réseaux électriques. Traditionnellement, l’équilibre entre la production et la consommation est assuré par les générateurs conventionnels fonctionnant aux combustibles fossiles. Or, ce moyen est économiquement inefficace et a un impact négatif sur l’environnement. La pénétration croissante des sources d’énergie renouvelable complique plus que jamais l’équilibre production-consommation puisqu’elles induisent davantage des fluctuations, ce qui accroît le besoin en réserves de contrôle à réaction rapide. La réponse à la demande est une des solutions efficaces, économiques et à impact environnemental positif pouvant prendre part dans la provision des réserves de contrôle, surtout avec le développement des technologies et techniques des réseaux intelligents. En particulier, les charges thermiquement contrôlées (TCLs) sont des candidats potentiels car elles sont nombreuses et largement distribuées dans le réseau électrique. De plus, les TCLs sont des appareils à action rapide et peuvent être gérées sans compromettre le confort du client. L’objectif principal de ce travail est l’implication des TCLs dans la provision du contrôle primaire et secondaire de la fréquence ainsi que la régulation de la tension. Par conséquent, un défi important consiste à développer une stratégie de contrôle fiable et à réaction rapide pour une participation efficace des TCLs dans de tels services auxiliaires. De plus, cela devrait être fait en tenant compte du confort du client, de l’usure des appareils et des problèmes liés aux cycles courts. De plus, une estimation et prévision précises des réserves de contrôle disponibles offertes par les TCLs sont essentielles. A cette fin, une approche basée sur un réseau de neurones est proposée pour l’estimation et la prévision précises de la flexibilité disponible offerte par les TCLs. Une comparaison entre cette nouvelle approche et l’approche conventionnelle basée sur la chaîne de Markov montre une précision de prédiction supérieure de l’approche basée sur un réseau de neurones. Des méthodes de contrôle sont ensuite développées pour une gestion efficace et optimale d’une population de TCLs via un agrégateur afin d’obtenir une réponse collective imitant le comportement des générateurs conventionnels, tout en respectant les exigences des services de contrôle fournis. En particulier, la participation des TCLs au contrôle primaire de la fréquence est semi-autonome et ne repose pas sur une communication en temps réel bénéficiant de la réponse rapide des TCLs.----------Abstract Operating an electric power system is a balancing act as the equilibrium between generation and consumption must be maintained in real-time. Frequency and voltage are accurate indicators of imbalances, and therefore must be kept within permissible ranges for a stable operation of power systems. Traditionally, the generation-consumption balancing is provided by fossil-fueled conventional generators which are economically inefficient and environmentally unfriendly. The increasing penetration of renewable energy sources is making the balancing more challenging than ever as they induce further fluctuations, which in turn increase the need for fast-responding control reserves. Demand Response is one of the efficient, cost-effective and environment-friendly alternatives for taking part in the provision of control reserves, especially with the development of smart grid technologies and techniques. In particular, thermostatically controlled loads (TCLs) are potential candidates as they are numerous and widely distributed in the electrical network. Furthermore, TCLs are fast-acting devices and can be managed without compromising customer comfort. The main objective of this work is the implication of TCLs in the provision of primary and secondary frequency control as well as voltage regulation. In this way, an important challenge is to develop a reliable and fast reacting control strategy for an efficient participation of TCLs in such ancillary services. Furthermore, this should be done taking into account customer comfort, device wear and tear, and short cycling issues. Moreover, an accurate estimation and prediction of the available control reserves offered by TCLs are essential. To this aim, a neural network-based approach is proposed for the accurate estimation and prediction of the available flexibility offered by TCLs. A comparison between this new approach and the conventional Markov-Chain approach shows a superior prediction accuracy of the neural network-based approach. Control methods are then developed for an effective and optimal management of a population of TCLs through an aggregator in order to obtain a collective response that imitates the behaviour of conventional generators, while respecting the requirements of the provided control services. In particular, TCLs participation in primary frequency control is semi-autonomous with no reliance on real-time communication benefiting from TCLs fast response. The proposed control methods are characterized by a fast response and a very low communication burden, while the customer comfort is maintained, the switching number is minimized by proper prioritization of TCLs, and short-cycling is reduced by the division of TCLs into groups

Topics in Demand Response for Energy Management in Smart Grid

Future electricity grids will enable greater and more sophisticated demand side participation, which refers to the inclusion of mechanisms that enable dynamic modification of electricity demand into the operations of the electricity market, known as Demand Response (DR). The underlying information-flow infrastructures provided by the emerging smart grid enhance the interactions between customers and the market, by which DR will improve electricity grids in several aspects, e.g., by reducing peak demand and reducing need for expensive peaker plants, or by enabling demand to follow supply such as those from volatile renewable resources, etc. Many types of appliances provide flexibilities in power usage which can be viewed as demand response resources, and how to exploit such flexibilities to achieve the benefits offered by DR is a central challenge. In this dissertation, we design algorithms and architectures to bridge the gap between scheduling appliances and the benefits that DR can bring to electricity grid by utilizing the smart grid\u27s underlying information infrastructure. First, we focus on demand response within the consumer premise, where an energy management controller (EMC) schedules appliance operation on behalf of customers to save energy cost. We propose an optimization-based control scheme for the EMC in the building that integrates both the operational flexible appliances such as clothes washer/dryer, dish washer and plug-in electric vehicles (PEVs), but also the thermostatically controlled appliances such as HVAC (heating, ventilation, and air conditioning) systems together with the thermal mass of the building. Model predictive control is employed to account for uncertainty in electricity prices and weather information. Under time-varying pricing, scheduling appliances smartly using our scheme can incur notable energy cost saving for customers. As an alternative, we also propose a communication-based control approach which is a joint appliance access and scheduling scheme in which the control algorithms are embedded into the communication protocols used by appliances. The control scheme is based on a threshold maximum power consumption set by the EMC; and we discuss how this threshold can be chosen so that it integrates the availability of local distributed renewable energy resources.Then we investigate demand response in the retail market level which involves interactions between customers and utilities. Pricing-based control and direct load control (DLC) are two types of approaches that are used or envisioned for this level. To address pricing based control methods, we propose real-time pricing (RTP) signals that can be designed to work with customer premise EMCs. The interaction between these EMCs and the pricing-setting utilities is modeled as a Stackelberg game. We demonstrate that our proposed RTP scheme reduces peak load and alleviates rebound peaks that are the typical shortcomings in existing pricing approaches. To address DLC methods, we propose a distributed DLC scheme based on a two-layer communication network infrastructure for large-scale, aggregate DR implementations. In the proposed scheme, average consensus algorithms are employed to distributively allocate control tasks amongst EMCs so that local appliance scheduling within each home will eventually achieve the aggregated control task, i.e., to alleviate mismatch between electricity supply and demand.Finally, we study how demand response affects the wholesale electricity market. As is conventional when studying interactions between electricity generators, we employ the Cournot game model to analyze how DR aggregators may impact wholesale energy markets. To do so, we assume that DR aggregators employ a computationally efficient, centralized scheduling mechanism to manage deferrable load over a large aggregate set of consumers. The load reduction from deferrable load can be seen as `generation\u27 in terms of balancing the market and is compensated as such under current regulatory mandates. Thus, the DR aggregator competes with other generators in a Cournot-Nash manner to make a profit in the wholesale market; and electricity prices are consequently reduced. We provide equilibrium analysis of the wholesale market that includes DR aggregators and demonstrate that under certain conditions the equilibrium exists and is unique

Demand Response for Residential Appliances in a Smart Electricity Distribution Network: Utility and Customer Perspectives

This thesis introduces advanced Demand Response algorithms for residential appliances to provide benefits for both utility and customers. The algorithms are engaged in scheduling appliances appropriately in a critical peak day to alleviate network peak, adverse voltage conditions and wholesale price spikes also reducing the cost of residential energy consumption. Initially, a demand response technique via customer reward is proposed, where the utility controls appliances to achieve network improvement. Then, an improved real-time pricing scheme is introduced and customers are supported by energy management schedulers to actively participate in it. Finally, the demand response algorithm is improved to provide frequency regulation services

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

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

Modeling and Control for Packetized Energy Management

The overarching goal in power systems operations is to deliver energy in an efficient, reliable, and economical manner. To achieve this objective, the traditional power system operating paradigm is for generation to follow variable demand. As electrification and decarbonization policies are pursued, the levels of variable, renewable generation will increase, which will require that power system operator think beyond supply follows demand. This means that one needs to consider the potential flexibility provided by, for instance, internet-enabled, connected, and responsive loads, which are part of the broad class of behind-the-meter distributed energy resources (DERs). The research work presented in this dissertation is concerned with coordinating large populations of distributed energy resources (DERs) for providing services to the electric grid. DERs are flexible in the sense that their power consumption can be deferred in time, because DERs store energy in some form while serving the end-use customer. For example, electric water heaters store thermal energy in the form of hot-water in the tank. Therefore, aggregate fleets of DERs are an inexpensive source of virtual energy storage that the utilities can tap into for the purpose of balancing the variability in distributed renewable generation such as solar PV, wind etc. In this work, a novel, asynchronous and randomized load coordination scheme called packetized energy management (PEM) is considered. Packetized energy management is a device-driven scheme that uses a unique request-response mechanism for coordinating diverse fleets of DERs. The aggregate dynamics of PEM are captured using state-bin transition models. Parameter heterogeneity is incorporated by grouping together relatively similar DERs. Furthermore, a notion of state of charge can be attached to the aggregate that is representative of the energy content in the fleet by means of a low order model. This low order model is of interest to the utilities and grid operators since it allows them to design control trajectories for DER aggregations depending upon grid requirements and load forecasts. Furthermore, a cyber-physical platform is developed for the validation of aggregate models and control schemes. However, PEM modifies the normal behavior of DERs and for accurate prediction of load dynamics, the underlying customer driven end-use process must be modeled to sufficient accuracy. Moreover, the modeled end-use process must be identifiable from the available data. In this work, the focus is on the uncontrollable hot-water extraction from the tank of an electric water heater. It is relevant and of interest to independent system operators (ISO) since water extraction is not usually measured and only metered interval consumption data (kWh) is collected. This is achieved by designing an estimation strategy based on a stochastic model of the end-use consumption