Incentive based Residential Demand Aggregation

From the beginning of the twenty-first century, the electrical power industry has moved from traditional power systems toward smart grids. However, with the increasing amount of renewable energy resources integrated into the grid, there is a significant challenge in power system operation due to the intermittency and variability of the renewables. Therefore, the utilization of flexible and controllable demand-side resources to maintain power system efficiency and stability has become a fundamental goal of smart grid initiatives. Meanwhile, due to the development of communication and sensing technologies, intelligent demand-side management with automatic controls enables residential loads to participate in demand response programs. Therefore, the aggregate control of residential appliances is anticipated to be feasible technique in the near future, which will bring considerable benefits to both residential consumers and load-serving entities. Hence, this dissertation proposes a comprehensive optimal framework for incentive based residential demand aggregation. The contents of this dissertation include: 1) a hardware design of smart home energy management system, 2) a new model to assess the responsive residential demand to financial incentives, and 3) an online algorithm for scheduling residential appliances. The proposed framework is expected to generate optimal control strategies over residential appliances enrolled in incentive based DR programs in real time. To residential consumers, this framework will 1) provide easy-to-use smart energy management solution, 2) distribute financial rewards by their quantified contribution in DR events, and 3) maintain residents’ comfort-level expectations based on their energy usage preferences. To LSEs, this framework can 1) aggregate residential demand to enhance system reliability, stability and efficiency, and 2) minimize the total reward costs for executing incentive based DR programs. Since this framework benefits both load serving entities and residents, it can stimulate the potential capability of residential appliances enrolled in incentive based DR programs. Eventually, with the growing number of DR participants, this framework has the potential to be one of the most vital parts in providing effective demand-side ancillary services for the entire power system

Residential Energy Management for Renewable Energy Systems Incorporating Data-Driven Unravelling of User Behavior

The penetration of distributed energy resources (DERs) such as photovoltaic (PV) at the residential level has increased rapidly over the past year. It will inevitably induce a paradigm shift in end-user and operations of local energy markets. The energy community with high integration of DERs initiative allows its users to manage their generation (for prosumers) and consumption more efficiently, resulting in various economic, social, and environmental benefits. Specifically, the local energy communities and their members can legally engage in energy generation, distribution, supply, consumption, storage, and sharing to increase levels of autonomy from the power grid, advance energy efficiency, reduce energy costs, and decrease carbon emissions. Reducing energy consumption costs is difficult for residential energy management without understanding the users' preferences. The advanced measurement and communication technologies provide opportunities for individual consumers/prosumers and local energy communities to adopt a more active role in renewable-rich smart grids. Non-intrusive load monitoring (NILM) monitors the load activities from a single point source, such as a smart meter, based on the assumption that different appliances have different power consumption levels and features. NILM can extract the users' load consumption from the smart meter to support the development of the smart grid for better energy management and demand response (DR). Yet to date, how to design residential energy management, including home energy management systems (HEMS) and community energy management systems (CEMS), with an understanding of user preferences and willingness to participate in energy management, is still far from being fully investigated. This thesis aims to develop methodologies for a resident energy management system for renewable energy systems (RES) incorporating data-driven unravelling of the user's energy consumption behaviour

Optimatization of hybrid renewable energy systems on isolated microgrids : a smart grid approach

Tese de doutoramento, Sistemas Sustentáveis de Energia, Universidade de Lisboa, Faculdade de Ciências, 2016The energy systems of small isolated communities face great challenges related to their autonomy and resilience, when looking for a sustainable energy future. Hybrid renewable energy systems, composed from different technologies, partially or totally renewable, potentiates a growing security of supply for these isolated micro-communities. Moreover, with a smart grid approach, the possibility to reschedule part of the electricity load is seen as a promising opportunity to delay further investments on the grid’s power capacity, enabling a better grid management, through peak load control, but also to promote a more efficient use of endogenous resources, maximizing renewable penetration. To identify the micro-communities main energy challenges, a literature review was taken, reporting the design and implementation of isolated hybrid renewable energy systems. Since electricity and heat energy vectors can be, in part, assured by endogenous resources, a methodology to optimize demand response on isolated hybrid renewable energy systems was developed, using the electric backup of solar thermal systems for domestic hot water supply as flexible loads. This approach is intended to increase energy efficiency of the energy system, reducing grid operation costs and associated CO2 emissions. A model of the electric impact of the implementation of solar thermal systems and heat pumps for domestic hot water supply was developed and tested for the Corvo Island case study, a small and isolated microgrid, located in the mid-Atlantic with around 400 inhabitants and a diesel power plant. An impact of 60% on peak load and 7% on annual electricity demand was found. In order to tackle this significant impact in the grid, a model for optimizing the economic dispatch of the island was developed, testing multiple demand response approaches to the backup loads, from heuristics to genetic algorithms, having this last one performed best to control the peak load and minimize the operation costs. Nonetheless, there was the need to compare and validate the demand response optimization strategies of this developed model with other available modeling tools, which in the end presented similar results. As the pillar of this thesis is the optimization of hybrid renewable energy systems, the influence of the uncertainties associated to renewables forecast had to be studied, in particular its impact on the demand response scheduling. Wind uncertainties demonstrated to have a greater impact on the grid than the solar ones. Finally, the methodology developed incrementally along the thesis and validated in Corvo Island, was tested on different scales and types of isolated systems. It demonstrated to be especially suitable for small systems with less than 20 MW power installed and over 25% renewable generation, with mostly residential load profiles

Smart home energy management: An analysis of a novel dynamic pricing and demand response aware control algorithm for households with distributed renewable energy generation and storage

Home energy management systems (HEMS) technology can provide a smart and efficient way of optimising energy usage in residential buildings. One of the main goals of the Smart Grid is to achieve Demand Response (DR) by increasing end users’ participation in decision making and increasing the level of awareness that will lead them to manage their energy consumption in an efficient way. This research presents an intelligent HEMS algorithm that manages and controls a range of household appliances with different demand response (DR) limits in an automated way without requiring consumer intervention. In addition, a novel Multiple Users and Load Priority (MULP) scheme is proposed to organise and schedule the list of load priorities in advance for multiple users sharing a house and its appliances. This algorithm focuses on control strategies for controllable loads including air-conditioners, dishwashers, clothes dryers, water heaters, pool pumps and electrical vehicles. Moreover, to investigate the impact on efficiency and reliability of the proposed HEMS algorithm, small-scale renewable energy generation facilities and energy storage systems (ESSs), including batteries and electric vehicles have been incorporated. To achieve this goal, different mathematical optimisation approaches such as linear programming, heuristic methods and genetic algorithms have been applied for optimising the schedule of residential loads using different demand side management and demand response programs as well as optimising the size of a grid connected renewable energy system. Thorough incorporation of a single objective optimisation problem under different system constraints, the proposed algorithm not only reduces the residential energy usage and utility bills, but also determines an optimal scheduling for appliances to minimise any impacts on the level of consumer comfort. To verify the efficiency and robustness of the proposed algorithm a number of simulations were performed under different scenarios. The simulations for load scheduling were carried out over 24 hour periods based on real-time and day ahead electricity prices. The results obtained showed that the proposed MULP scheme resulted in a noticeable decrease in the electricity bill when compared to the other scenarios with no automated scheduling and when a renewable energy system and ESS are not incorporated. Additionally, further simulation results showed that widespread deployment of small scale fixed energy storage and electric vehicle battery storage alongside an intelligent HEMS could enable additional reductions in peak energy usage, and household energy cost. Furthermore, the results also showed that incorporating an optimally designed grid-connected renewable energy system into the proposed HEMS algorithm could significantly reduce household electricity bills, maintain comfort levels, and reduce the environmental footprint. The results of this research are considered to be of great significance as the proposed HEMS approach may help reduce the cost of integrating renewable energy resources into the national grid, which will be reflected in more users adopting these technologies. This in turn will lead to a reduction in the dependence on traditional energy resources that can have negative impacts on the environment. In particular, if a significant proportion of households in a region were to implement the proposed HEMS with the incorporation of small scale storage, then the overall peak demand could be significantly reduced providing great benefits to the grid operator as well as the households

Novel Conceptual Architecture for the Next-Generation Electricity Markets to Enhance a Large Penetration of Renewable Energy

[EN] A transition to a sustainable energy system is essential. In this context, smart grids represent the future of power systems for efficiently integrating renewable energy sources and active consumer participation. Recently, different studies were performed that defined the conceptual architecture of power systems and their agents. However, these conceptual architectures do not overcome all issues for the development of new electricity markets. Thus, a novel conceptual architecture is proposed. The transactions of energy, operation services, and economic flows among the agents proposed are carefully analysed. In this regard, the results allow setting their activities' boundaries and state their relationships with electricity markets. The suitability of implementing local electricity markets is studied to enforce competition among distributed energy resources by unlocking all the potential that active consumers have. The proposed architecture is designed to offer flexibility and efficiency to the system thanks to a clearly defined way for the exploitation of flexible resources and distributed generation. This upgraded architecture hereby proposed establishes the characteristics of each agent in the forthcoming markets and studies to overcome the barriers to the large deployment of renewable energy sources.This work was supported by the Ministerio de Economia, Industria, y Competitividad (Spanish Government) under research project ENE-2016-78509-C3-1-P, and EU FEDER funds. The authors received funds from these grants for covering the costs to publish in open access. This work was also supported by the Spanish Ministry of Education under the scholarship FPU16/00962.Rodríguez-García, J.; Ribó-Pérez, DG.; Álvarez, C.; Peñalvo-López, E. (2019). Novel Conceptual Architecture for the Next-Generation Electricity Markets to Enhance a Large Penetration of Renewable Energy. 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Modelling of Distributed Energy Components and Optimization of Energy Vector Dispatch within Smart Energy Systems

The smart energy system concept provides an integrated framework for the adoption of renewable energy resources and novel energy technologies, such as distributed battery energy storage systems and electric vehicles. In this effort, large-scale transition towards smart energy systems can significantly reduce the environmental emissions of energy production, while leveraging the compatible operation of numerous distributed grid components to improve upon the energy utility, reliability, and flexibility of existing power grids. Most importantly, transitioning from fossil fuels to renewable energy resources provides environmental benefits within both the building and transportation sectors, which must adapt to address both increasing pressure from international climate change-related policy-making, as well as to meet the increasing power demands of future generations. In the case of building operation, the transition towards future energy systems consequently result in the adoption of decentralized energy networks as well as various distributed energy generation, conversion, and storage technologies. As such, there is significant potential for existing systems to adopt more economic and efficient operating strategies, which may manifest in novel operational modes such as demand-response programs, islanded operation, and optimized energy vector dispatch within local systems. Furthermore, new planning and design considerations can provide economic, environmental, and energy efficiency benefits. While these potential benefits have been justified in existing literature, there is still a strong research need to quantify the impacts of optimal building operation within these criteria, under a smart energy system context. Meanwhile, the transportation sector may benefit from the smart energy network concept by leveraging electric mobility technologies and by transitioning vehicle charging demand onto the grid’s electricity network. In this transition, the emissions associated with fossil fuel consumption are displaced by grid-generated electricity, much of which may be derived from zero-emission resources in systems containing high renewable generation capacities. While small electric vehicle fleets have currently been successfully integrated into the grid, higher market penetration rates of electric vehicles demand significantly more charging infrastructure. In consideration of the consequences of various electric vehicle charging modes resulting from large-scale mobility electrification, there is a gap in the literature for the planning and design of charging infrastructure for facilitating interactions between electric vehicle fleets and future smart energy network systems. Within the work presented in this thesis, quantitative analysis has been presented for the potential for optimal building operation between complementary commercial and residential building types. From this, the economic and environmental benefits of applying the principles of smart energy systems within mixed residential and commercial hubs have been evaluated at reductions of 61.2% and 1.29%, respectively, under the context of an Ontario, Canada case study. Furthermore, reduced installation of local energy storage systems and consumption of grid-derived electricity were reduced by 6.7% and 13.8%, respectively, in comparison against base case scenarios in which buildings were operated independent of the proposed microgrid configuration. Meanwhile, the investigative work for the role of charging infrastructure in electric vehicle integration within smart energy systems provided insight into the power flow characteristics required to facilitate advanced electric vehicle charging modes. Most importantly, the work demonstrated limitations to the controlled/smart charging and the vehicle-to-grid charging modes imposed by charging port availability, electric vehicle plug-in durations, and maximum power flow characteristics. These results have highlighted the need for charging infrastructure to emulate the availability and fast response characteristics of stationary energy storage systems for successful vehicle-to-grid implementation, as well as the need for maximum power flow limitations for charging infrastructure to be well above the current level 2 standard for home- and workplace-charging

Innovations in energy and climate policy: lessons from Vermont

We ask in this article: how can planners and policymakers replicate Vermont’s energy and climate policies? We begin by explaining the research methods utilized for this article—mainly research interviews with a pool of experts, coupled with a targeted literature review. We then analyze the success of Vermont energy policy across four areas: energy efficiency, renewable energy, the smart grid, and energy governance. The following sections first explain how Vermont accomplished these successes, next identify a number of remaining barriers and elements of Vermont’s approach that may not be replicable, and finally present the article’s conclusions

Open-Source, Open-Architecture SoftwarePlatform for Plug-InElectric Vehicle SmartCharging in California

This interdisciplinary eXtensible Building Operating System–Vehicles project focuses on controlling plug-in electric vehicle charging at residential and small commercial settings using a novel and flexible open-source, open-architecture charge communication and control platform. The platform provides smart charging functionalities and benefits to the utility, homes, and businesses.This project investigates four important areas of vehicle-grid integration research, integrating technical as well as social and behavioral dimensions: smart charging user needs assessment, advanced load control platform development and testing, smart charging impacts, benefits to the power grid, and smart charging ratepayer benefits

Demand Response Strategy Based on Reinforcement Learning and Fuzzy Reasoning for Home Energy Management

As energy demand continues to increase, demand response (DR) programs in the electricity distribution grid are gaining momentum and their adoption is set to grow gradually over the years ahead. Demand response schemes seek to incentivise consumers to use green energy and reduce their electricity usage during peak periods which helps support grid balancing of supply-demand and generate revenue by selling surplus of energy back to the grid. This paper proposes an effective energy management system for residential demand response using Reinforcement Learning (RL) and Fuzzy Reasoning (FR). RL is considered as a model-free control strategy which learns from the interaction with its environment by performing actions and evaluating the results. The proposed algorithm considers human preference by directly integrating user feedback into its control logic using fuzzy reasoning as reward functions. Q-learning, a RL strategy based on a reward mechanism, is used to make optimal decisions to schedule the operation of smart home appliances by shifting controllable appliances from peak periods, when electricity prices are high, to off-peak hours, when electricity prices are lower without affecting the customer’s preferences. The proposed approach works with a single agent to control 14 household appliances and uses a reduced number of state-action pairs and fuzzy logic for rewards functions to evaluate an action taken for a certain state. The simulation results show that the proposed appliances scheduling approach can smooth the power consumption profile and minimise the electricity cost while considering user’s preferences, user’s feedbacks on each action taken and his/her preference settings. A user-interface is developed in MATLAB/Simulink for the Home Energy Management System (HEMS) to demonstrate the proposed DR scheme. The simulation tool includes features such as smart appliances, electricity pricing signals, smart meters, solar photovoltaic generation, battery energy storage, electric vehicle and grid supply.Peer reviewe