Credit-based distributed real-time energy storage sharing management

Abstract: In this paper, energy storage sharing among a group of cooperative households with integrated renewable generations in a grid-connected microgrid is studied. In such a microgrid, a group of households, who are willing to cooperatively operate a shared energy storage via a central coordinator, aims to minimize their long term time-averaged costs, by jointly taking into account the operational constraints of the shared energy storage, the stochastic solar power generations and the time-varying load demands from all households, as well as the fluctuating electricity prices. This energy management problem, which comprises storage management and load control, is first formulated as a constrained stochastic programming problem. Based on the Lyapunov optimization theory, a distributed real-time sharing control algorithm is proposed to solve the constrained stochastic programming problem without requiring any statistical knowledge of the stochastic renewable energy generations and the uncertain power loads. The credit-based distributed sharing algorithm, in which each household independently solves a simple convex optimization problem without requiring any statistics of the system, is designed to quickly adapt to the system dynamics while facilitating a fair allocation of the shared energy storage with respect to individual households’ energy contributions. The performance gap of the proposed low-complexity distributed sharing algorithm is evaluated via theoretical analysis. Numerical simulations using a practical system setup are conducted to investigate the effectiveness of the proposed sharing control algorithm in terms of energy cost saving and fairness. The simulation results show that the proposed credit-based distributed sharing algorithm can not only save power consumption cost by coordinating the use the shared battery among households in a fair manner but also improve the utilization of renewable energy generation

A distributed real-time control algorithm for energy storage sharing

Abstract: In this paper, energy storage sharing among a group of cooperative households with integrated renewable generations in a grid-connected microgrid in the presence of dynamic electricity pricing is studied. In such a microgrid, a group of households, who are willing to cooperatively operate a shared energy storage system (ESS) via a central coordinator, aims to minimize their long term time-averaged costs, by jointly taking into account the operational constraints of the shared energy storage, the stochastic solar energy generations and time-varying load requests from all households, as well as the fluctuating electricity prices. We formulate this energy management problem, which comprises storage management and load control, as a constrained stochastic programming problem. Based on the Lyapunov theory, a distributed real-time sharing control algorithm is proposed to provide a suboptimal solution for the constrained stochastic programming problem without requiring any system statistics. The proposed distributed real-time sharing control algorithm, in which each household independently solves a simple convex optimization problem in each time slot, can quickly adapt to the system dynamics. The performance of the proposed low-complexity sharing control algorithm is evaluated via both theoretical analysis and numerical simulations. By comparing with a greedy sharing algorithm and the distributed ESSs case, it is shown that the proposed distributed sharing control algorithm outperforms in terms of both cost saving and renewable energy generation utilization

Electric Vehicle (EV)-Assisted Demand-Side Management in Smart Grid

While relieving the dependency on diminishing fossil fuels, Electric Vehicles (EVs) provide a promising opportunity to realise an eco-friendly and cost-effective means of transportation. However, the enormous electricity demand imposed by the wide-scale deployment of EVs can put power infrastructure under critical strain, potentially impacting the efficiency, resilience, and safety of the electric power supply. Interestingly, EVs are deferrable loads with flexible charging requirements, making them an ideal prospect for the optimisation of consumer demand for energy, referred to as demand-side management. Furthermore, with the recent introduction of Vehicle-to-Grid (V2G) technology, EVs are now able to act as residential battery systems, enabling EV customers to store energy and use them as backup power for homes or deliver back to the grid when required. Hence, this thesis studies Electric Vehicle (EV)-assisted demand-side management strategies to manage peak electricity demand, with the long-term objective of transforming to a fully EV-based transportation system without requiring major upgrades in existing grid infrastructure. Specifically, we look at ways to optimise residential EV charging and discharging for smart grid, while addressing numerous requirements from EV customer's perspective and power system's perspective. First, we develop an EV charge scheduling algorithm with the objective of tracking an arbitrary power profile. The design of the algorithm is inspired by water-filling theory in communication systems design, and the algorithm is applied to schedule EV charging following a day-ahead renewable power generation profile. Then we extend that algorithm by incorporating V2G operation to shape the load curve in residential communities via valley-filling and peak-shaving. In the proposed EV charge-discharge algorithm, EVs are distributedly coordinated by implementing a non-cooperative game. Our numerical simulation results demonstrate that the proposed algorithm is effective in flattening the load curve while satisfying all heterogeneous charge requirements across EVs. Next, we propose an algorithm for network-aware EV charging and discharging, with an emphasis on both EV customer economics and distribution network aspects. The core of the algorithm is a Quadratic Program (QP) that is formulated to minimise the operational costs accrued to EV customers while maintaining distribution feeder nodal voltage magnitudes within prescribed thresholds. By means of a receding horizon control approach, the algorithm implements the respective QP-based EV charge-discharge control sequences in near-real-time. Our simulation results demonstrate that the proposed algorithm offers significant reductions in operational costs associated with EV charging and discharging, while also mitigating under-voltage and over-voltage conditions arising from peak power flows and reverse power flows in the distribution network. Moreover, the proposed algorithm is shown to be robust to non-deterministic EV arrivals and departures. While the previous algorithm ensures a stable voltage profile across the entire distribution feeder, it is limited to balanced power distribution networks. Therefore, we next extend that algorithm to facilitate EV charging and discharging in unbalanced distribution networks. The proposed algorithm also supports distributed EV charging and discharging coordination, where EVs determine their charge-discharge profiles in parallel, using an Alternating Direction Method of Multipliers (ADMM)-based approach driven by peer-to-peer EV communication. Our simulation results confirm that the proposed distributed algorithm is computationally efficient when compared to its centralised counterpart. Moreover, the proposed algorithm is shown to be successful in terms of correcting any voltage violations stemming from non-EV load, as well as, satisfying all EV charge requirements without causing any voltage violations

Upscaling energy control from building to districts: current limitations and future perspectives

Due to the complexity and increasing decentralisation of the energy infrastructure, as well as growing penetration of renewable generation and proliferation of energy prosumers, the way in which energy consumption in buildings is managed must change. Buildings need to be considered as active participants in a complex and wider district-level energy landscape. To achieve this, the authors argue the need for a new generation of energy control systems capable of adapting to near real-time environmental conditions while maximising the use of renewables and minimising energy demand within a district environment. This will be enabled by cloud-based demand-response strategies through advanced data analytics and optimisation, underpinned by semantic data models as demonstrated by the Computational Urban Sustainability Platform, CUSP, prototype presented in this paper. The growing popularity of time of use tariffs and smart, IoT connected devices offer opportunities for Energy Service Companies, ESCo’s, to play a significant role in this new energy landscape. They could provide energy management and cost savings for adaptable users, while meeting energy and CO2 reduction targets. The paper provides a critical review and agenda setting perspective for energy management in buildings and beyond

Optimal dispatch of uncertain energy resources

The future of the electric grid requires advanced control technologies to reliably integrate high level of renewable generation and residential and small commercial distributed energy resources (DERs). Flexible loads are known as a vital component of future power systems with the potential to boost the overall system efficiency. Recent work has expanded the role of flexible and controllable energy resources, such as energy storage and dispatchable demand, to regulate power imbalances and stabilize grid frequency. This leads to the DER aggregators to develop concepts such as the virtual energy storage system (VESS). VESSs aggregate the flexible loads and energy resources and dispatch them akin to a grid-scale battery to provide flexibility to the system operator. Since the level of flexibility from aggregated DERs is uncertain and time varying, the VESSs’ dispatch can be challenging. To optimally dispatch uncertain, energy-constrained reserves, model predictive control offers a viable tool to develop an appropriate trade-off between closed-loop performance and robustness of the dispatch. To improve the system operation, flexible VESSs can be formulated probabilistically and can be realized with chance-constrained model predictive control. The large-scale deployment of flexible loads needs to carefully consider the existing regulation schemes in power systems, i.e., generator droop control. In this work first, we investigate the complex nature of system-wide frequency stability from time-delays in actuation of dispatchable loads. Then, we studied the robustness and performance trade-offs in receding horizon control with uncertain energy resources. The uncertainty studied herein is associated with estimating the capacity of and the estimated state of charge from an aggregation of DERs. The concept of uncertain flexible resources in markets leads to maximizing capacity bids or control authority which leads to dynamic capacity saturation (DCS) of flexible resources. We show there exists a sensitive trade-off between robustness of the optimized dispatch and closed-loop system performance and sacrificing some robustness in the dispatch of the uncertain energy capacity can significantly improve system performance. We proposed and formulated a risk-based chance constrained MPC (RB-CC-MPC) to co-optimize the operational risk of prematurely saturating the virtual energy storage system against deviating generators from their scheduled set-point. On a fast minutely timescale, the RB-CC-MPC coordinates energy-constrained virtual resources to minimize unscheduled participation of ramp-rate limited generators for balancing variability from renewable generation, while taking into account grid conditions. We show under the proposed method it is possible to improve the performance of the controller over conventional distributionally robust methods by more than 20%. Moreover, a hardware-in-the-loop (HIL) simulation of a cyber-physical system consisting of packetized energy management (PEM) enabled DERs, flexible VESSs and transmission grid is developed in this work. A predictive, energy-constrained dispatch of aggregated PEM-enabled DERs is formulated, implemented, and validated on the HIL cyber-physical platform. The experimental results demonstrate that the existing control schemes, such as AGC, dispatch VESSs without regard to their energy state, which leads to unexpected capacity saturation. By accounting for the energy states of VESSs, model-predictive control (MPC) can optimally dispatch conventional generators and VESSs to overcome disturbances while avoiding undesired capacity saturation. The results show the improvement in dynamics by using MPC over conventional AGC and droop for a system with energy-constrained resources

City-Friendly Smart Network Technologies and Infrastructures: The Spanish Experience

Efficient, resilient, and sustainable electricity delivery is a key cornerstone in increasingly large and complex urban environments, where citizens expect to keep or rise their living standards. In this context, cost-effective and ubiquitous digital technologies are driving the transformation of existing electrical infrastructures into truly smart systems capable of better providing the services a low-carbon society is demanding. The goal of this paper is twofold: 1) to review the dramatically evolving landscape of power systems, from the old framework based on centralized generation and control, aimed at serving inelastic customers through alternating current (ac) transmission networks and one-way distribution feeders, to a new paradigm centered mainly around two main axes: renewable generation, both centralized and distributed, and active customers (prosumers), interacting with each other through hybrid ac/dc smart grids; 2) to illustrate, through featured success stories, how several smart grid concepts and technologies have been put into practice in Spain over the last few years to optimize the performance of urban electrical assets

Scaling energy management in buildings with artificial intelligence

L'abstract è presente nell'allegato / the abstract is in the attachmen

Wireless network architecture for future smart grid machine to machine communications

Transformation of the conventional power grid into an efficient power delivery network is an important advance that will benefit consumers, business and the environment by providing improved integration of renewable energy, including solar and wind. A reliable, low latency communication system is a fundamental requirement for smart power grids. To achieve bidirectional energy distribution capability and to support diverse Smart Grid (SG) applications, the modern SG requires the capacity to handle the traffic generated by machine to machine (M2M) communication infrastructure. Successful integration of numerous SG applications, renewable energy sources and Electric Vehicles (EVs) into a conventional power grid would not be possible without a communication network that has been designed to support the needs of the new and innovative renewable power generation, distribution and storage technologies. While the legacy communication infrastructure, utilized to support the existing power network, fails to support all of the SG functionalities, Software Defined Networking (SDN), based on wireless communication systems, has the potential to provide an effective solution. SDN offers a range of features that fulfill the unique requirements of the SG applications. Being a new networking paradigm, SDN remains to be implemented for SG M2M communication scenarios and there remain a number of challenges that need to be overcome. M2M communication protocols and standards provide a starting point for the broader development of SG communication networks that can be enhanced by abstracting high-level network functionalities. The aim of this research was to carry out an in-depth study on the future SG communication networks and to propose solutions to identified limitations of existing communication networks. Keeping this intention in mind, the study first focuses on the SG application modeling techniques based on the traffic requirements and power supply load profiles. To address the dynamicity of the traffic model and demand load curve, a series of analytical models and smart algorithms were developed. SG application models were developed and evaluated using a range of scenarios reflecting typical usage. Heterogenous network architectures and efficient traffic models were developed to identify an appropriate wireless communication technology and to maximize the network performance for major SG applications. However, a careful observation of the communication networks ability to manage and control the diverse M2M communications reveals that the inadequate dynamic communication network configuration capability would be a problem for future SG applications. M2M communication protocols and standards provide a starting point for the broader development of SG communication networks that can be enhanced by abstracting high-level network functionalities. To realize the full potential of the SGs and deployment scenarios it is essential to analyze the major applications and key requirements to develop those applications. Also, it might be necessary to select an appropriate communication technology for each of the power system domains. The study first focuses on the SG application modeling techniques based on the traffic requirement and load supply profiles of the power system. To address dynamicity of the traffic model and demand load curve, a series of analytical models and smart algorithms were developed. The developed SG application models were further evaluated using simulation scenarios and a test bed model. The challenge of selecting an appropriate wireless communication technology and maximizing network performance for major SG applications was handled by developing multiple heterogenous network architectures and efficient traffic models. A comprehensive literature review of the state of the art of SG applications and standards was carried out to develop robust network models utilizing diverse communication technologies. The literature survey immensely helped to develop two novel SG application models, Zigbee based Pilot protection scheme for a smart distribution grid and Vehicle to Grid (V2G) smart load management scheme. Application modelling included detail traffic modelling, developing smart algorithms, analytical models, user load profile analysis, simulation models and test bed setups. Furthermore, a novel WiMax Ranging scheme is presented to improve the random-access mechanism for various periodic M2M applications supported by extensive simulation based performance analysis. Future SGs will be overwhelmed by an excessive number of sensor devices that collect various data related to the power system. In a SG Neighborhood Area Network (NAN), wireless sensor networks (WSNs) will play a key role in the development of major SG applications. The application centric WSNs require complex configurations such as well-defined access techniques, transmission and security protocols. Challenges also include development of appropriate routing protocols to tackle resource limitations and delay caused by decentralized WSNs and ad hoc based packet forwarding techniques. A careful observation of manageability and controllability of the diverse M2M network reveals that the inadequate dynamic network configuration capability of the existing SG communication network would be a key bottleneck for future SG. Thus, a novel WSN based communication framework is presented exploiting the emerging SDN networking paradigm. SDN would be beneficial for SGs in many ways. By decoupling the control plane and data forwarding plane, SDN facilitates real-time control and integration of network services and applications that can reach down into the network through the controller hierarchy. A higher degree of control over the overall SG communication network would be achievable via the dynamic programmability provided by SDN. The SDN based WSN network must be robust enough to support the adaptive energy dispatching capacity of the modern power system. The proposed communication framework incorporates novel communication features to separate the control plane and data forwarding plane within the SG communication network. This includes detailed modeling of the control and data plane communication parameters to support both delay sensitive and delay tolerant SG applications. The unique SDN features offers a platform to accommodate maximum number of SG applications with highest controllability and manageability. The performance of the SDN based future SG network is evaluated using a simulation scenario that considers realistic user load profiles, wireless standards, the SG premises geographical area and the state of the art of the SG standards. Although the control plane enables a global view of the data plane and provides a centralized platform to control and deploy new services, physically a single controller in the controller would not be practical for SG networks. The challenges arise in terms of scalability, security and reliability, particularly in a SG environment. To increase the efficiency of the proposed SDN based WSNs for the SG NAN, the study proposed distributed controllers with a comprehensive analytical model that optimizes the number of distributed controllers to enhance performance of the proposed communication framework in the NAN domain. The proposed framework along with the analytical model derive several solutions, such as the minimum number of controllers to support the switches and M2M devices, accommodate SG applications and a differentiated flow processing technique to support all traffic types within the network. Lastly, the study focuses on developing SDN-based application specific traffic models for the smart distribution grid. The thesis focuses on three major issues while developing a future SG communication system. Firstly, its identifies major applications and their traffic requirements at different domains of the SG. Appropriate traffic models were developed by designing robust wireless communication network models. Also, application centric smart optimization techniques are adopted to achieve maximum performance and presented with simulation results, statistical analysis and a test bed result analysis. Secondly, to facilitate the centralized controllability and programmability for supporting diverse SG applications within the SG, a novel WSNs communication framework is presented exploiting the next generation SDN paradigm. Both delay sensitive and delay tolerant SG applications were considered based on the traffic requirement to develop the SDN based WSN communication framework in the SG NAN. Smart algorithms were developed at the SDN based WSN application layer to accommodate a large number of SG applications. The framework feasibility is demonstrated by the simulations carried out to verify the model and provide a statistical analysis. Thirdly, the thesis focuses on developing a novel analytical model that can be used to determine the optimal number of distributed controllers and switches in a SG NAN domain. The proposed application centric traffic modelling techniques, SDN based wireless communication framework and analytical models in this thesis can be adapted for research into other communication networks, particularly those that are begin developed for the Internet of Things and other forms of M2M communications. Also, due to the technology agonistic characteristics of the analytical and traffic models, they can be used in the development of various wireless networks, particularly those that focus on wireless sensor networks, more generally than the broader Internet of Things

Advanced Mechanism Design for Electric Vehicle Charging Scheduling in the Smart Infrastructure

Electric vehicle (EV) continues to grow rapidly due to low emission and high intelligence. This thesis considers a smart infrastructure (SI) as an EV-centered ecosystem, which is an integrated and connected multi-modal network involving interacting intelligent agents, such as EVs, charging facilities, electric power grids, distributed energy resources, etc. The system modeling paradigm is derived from distributed artificial intelligence and modelled as multi-agent systems (MAS), where the agents are self-interested and reacting strategically to maximize their own benefits. The integration, interaction, and coordination of EVs with SI components will raise various features and challenges on the transportation efficiency, power system stability, and user satisfaction, as well as opportunities provided by optimization, economics, and control theories, and other advanced technologies to engage more proactively and efficiently in allocating the limited charging resources and collaborative decision-making in a market environment. A core challenge in such an EV ecosystem is to trade-off the two objectives of the smart infrastructure, of system-wide efficiency and at the same time the social welfare and individual well-being against agents’ selfishness and collective behaviors. In light of this, scheduling EVs' charging activities is of great importance to ensure an efficient operation of the smart infrastructure and provide economical and satisfactory charging experiences to EV users under the support of two-way flow of information and energy of charging facilities. In this thesis, we develop an advanced mechanism design framework to optimize the charging resource allocation and automate the interaction process across the overall system. The key innovation is to design specific market-based mechanisms and interaction rules, integrated with concepts and principles of mechanism design, scheduling theory, optimization theory, and reinforcement learning, for charging scheduling and dynamic pricing problem in various market structures. Specifically, this research incorporates three synergistic areas: (1) Mathematical modelling for EV charging scheduling. We have developed various mixed-integer linear programs for single-charge with single station, single-charge with multiple stations, and multi-charge with multiple stations in urban or highway environments. (2) Market-based mechanism design. Based on the proposed mathematical models, we have developed particular market-based mechanisms from the resource provider’s prospective, including iterative bidding auction, incentive-compatible auction, and simultaneous multi-round auction. These proposed auctions contain bids, winner determination models, and bidding procedure, with which the designer can compute high quality schedules and preserve users’ privacy by progressively eliciting their preference information as necessary. (3) Reinforcement learning-based mechanism design. We also proposed a reinforcement mechanism design framework for dynamic pricing-based demand response, which determines the optimal charging prices over a sequence of time considering EV users’ private utility functions. The learning-based mechanism design has effectively improved the long-term revenue despite highly-uncertain requests and partially-known individual preferences of users. This Ph.D. dissertation presents a market prospective and unlocks economic opportunities for MAS optimization with applications to EV charging related problems; furthermore, applies AI techniques to facilitate the evolution from manual mechanism design to automated and data-driven mechanism design when gathering, distributing, storing, and mining data and state information in SI. The proposed advanced mechanism design framework will provide various collaboration opportunities with the research expertise of reinforcement learning with innovative collective intelligence and interaction rules in game theory and optimization tools, as well as offers research thrust to more complex interfaces in intelligent transportation system, smart grid, and smart city environments