Commercial integration of storage and responsive demand to facilitate wind energy on the Shetland Islands

The Northern Isles New Energy Solutions (NINES) project seeks to implement Active Network Management (ANM) on the Shetland Islands in a manner which reduces customers’ energy consumption, lowers peak demand and facilitates an increase in the proportion of electricity from wind, in order to take advantage of the unique wind resource of the islands. This presentation focuses on the commercial frameworks and trading arrangements necessary to permit additional wind capacity onto the islanded network through the active use of storage and responsive demand technologies. The network is modelled using a Dynamic Optimal Power Flow (DOPF) framework, which allows the unit scheduling of different combinations of generation, storage and demand to be optimised according to different optimisation goals. This is used as a foundation to explore the value of wind energy and storage in meeting the long-term goals of the network, the forms of trading and markets which may be used to contract services, and the potential for responsive demand to facilitate different forms of connection agreements and curtailment strategies for new wind farms. In modelling the Shetland network using Dynamic Optimal Power Flow (DOPF), the optimum unit commitment schedule is determined across a daily horizon for different network topologies, including variable levels of wind generation, storage and demand-side response - primarily storage heaters and water tanks controllable by the Distribution System Operator via Active Network Management. This informs the level of wind generation which may be accepted onto the network, and allows the creation and testing of commercial agreements both for wind generators keen to utilise the unique resource of the islands, as well as allowing third-party operation of storage, and reducing the peak energy demand of domestic consumers. This allows a greater level of demand to be supplied by non-thermal sources through the time-shifting of demand against the availability of the wind resource. Support of the grid through reserve and response is considered in the context of maintaining system stability, with the aim of procuring services through third-party contractual arrangements. Data collected from the operational history of the islands and technology trials demonstrate the feasibility of these approaches and their potential applicability to other constrained distribution networks with the potential for high levels of wind generation. The data from trials of domestic storage equipment and modelling of wind curtailment demonstrate quantitatively the ways in which commercial integration of modern storage and responsive demand can be used to increase the utilisation of wind energy on islanded networks, which may often have increased renewable resources but limited grid capacity. It is shown that there are a number of trading and connection agreements which can be used to contract for generation and ancillary services to meet these goals

Convex Optimization Approach to the Optimal Power Flow Problem in DC-Microgrids with Energy Storage

Humanity is currently facing a global energy crisis. This is due to the shortage in the conventional energy resources while the demand for energy is rising. In response to this crisis, research in designing more energy efficient systems has gained significant importance. The Microgrids (MGs) are one of the main key elements in giving significant momentum to efficient decentralized energy generation. From the perspective of MGs power management, economical scheduling for generators, energy storage, and demand loads are critical. Performance optimization processes are needed to minimize the operating costs while considering operational constraints. In this thesis, the optimal power flow problem for managing energy sources with storage devices is presented for dc microgrids. The power management model has been examined in various scenarios. One of them is based on a network of a six-bus power system, including an energy storage device coupling at a certain bus. The other scenario is based on the same model but including more energy storage devices. After analyzing the results of these scenarios, several conclusions have been made such as when the energy storage should charge/discharge to minimize costs. The study shows the feasibility of optimal power flow operation in DC microgrids

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

MPC for optimal dispatch of an AC-linked hybrid PV/wind/biomass/H2 system incorporating demand response

[EN] A Model Predictive Control (MPC) strategy based on the Evolutionary Algorithms (EA) is proposed for the optimal dispatch of renewable generation units and demand response in a grid-tied hybrid system. The generating system is based on the experimental setup installed in a Distributed Energy Resources Laboratory (LabDER), which includes an AC micro-grid with small scale PV/Wind/Biomass systems. Energy storage is by lead-acid batteries and an H2 system (electrolyzer, H2 cylinders and Fuel Cell). The energy demand is residential in nature, consisting of a base load plus others that can be disconnected or moved to other times of the day within a demand response program. Based on the experimental data from each of the LabDER renewable generation and storage systems, a micro-grid operating model was developed in MATLAB(C) to simulate energy flows and their interaction with the grid. The proposed optimization algorithm seeks the minimum hourly cost of the energy consumed by the demand and the maximum use of renewable resources, using the minimum computational resources. The simulation results of the experimental micro-grid are given with seasonal data and the benefits of using the algorithm are pointed out.Acevedo-Arenas, CY.; Correcher Salvador, A.; Sánchez-Diaz, C.; Ariza-Chacón, HE.; Alfonso-Solar, D.; Vargas-Salgado, C.; Petit-Suarez, JF. (2019). MPC for optimal dispatch of an AC-linked hybrid PV/wind/biomass/H2 system incorporating demand response. Energy Conversion and Management. 186:241-257. https://doi.org/10.1016/j.enconman.2019.02.044S24125718

Control and Communication Protocols that Enable Smart Building Microgrids

Recent communication, computation, and technology advances coupled with climate change concerns have transformed the near future prospects of electricity transmission, and, more notably, distribution systems and microgrids. Distributed resources (wind and solar generation, combined heat and power) and flexible loads (storage, computing, EV, HVAC) make it imperative to increase investment and improve operational efficiency. Commercial and residential buildings, being the largest energy consumption group among flexible loads in microgrids, have the largest potential and flexibility to provide demand side management. Recent advances in networked systems and the anticipated breakthroughs of the Internet of Things will enable significant advances in demand response capabilities of intelligent load network of power-consuming devices such as HVAC components, water heaters, and buildings. In this paper, a new operating framework, called packetized direct load control (PDLC), is proposed based on the notion of quantization of energy demand. This control protocol is built on top of two communication protocols that carry either complete or binary information regarding the operation status of the appliances. We discuss the optimal demand side operation for both protocols and analytically derive the performance differences between the protocols. We propose an optimal reservation strategy for traditional and renewable energy for the PDLC in both day-ahead and real time markets. In the end we discuss the fundamental trade-off between achieving controllability and endowing flexibility

Economic assessment of flexibility offered by an optimally controlled hybrid heat pump generator: a case study for residential building

Abstract The ongoing decarbonisation process of the current energy system, driven by the EU directives, requires that more renewable energy sources are integrated in the global energy mix, as well as policies promoting investments in new low-carbon technologies, energy efficiency and grid infrastructure. The technical integration of renewable energy sources into the existing power system is not straightforward, due to the intrinsic aleatory characteristics of renewable production, which make the power grid balance harder. To handle this issue, beside the traditional supply-side management, grid flexibility can also be provided by enabling the active participation of the demand-side in power system operational procedures, by means of the so-called demand-side management (DSM). The present paper is aimed at assessing the ability of a cost-optimal control strategy, based on model predictive control, to activate demand-response (DR) actions in a residential building equipped with a hybrid heat pump generator coupled with a water thermal storage. Hourly electricity prices are considered as external signals from the grid driving the demand response actions. It is shown that the thermal energy storage turns out to be an effective way to improve the controller performances and make the system more flexible and able to provide services to the power grid. A daily cost-saving up to 35% and 15% have been highlighted with a 1 m3 0.5.m3 tanks, respectively. Finally, the achievable flexibility is shown to be strictly dependent on the storage capacity and operations, which in turn are affected by the generators sizing

Optimal Management of an Integrated Electric Vehicle Charging Station under Weather Impacts

The focus of this Dissertation is on developing an optimal management of what is called the “Integrated Electric Vehicle Charging Station” (IEVCS) comprising the charging stations for the Plug-in Electric Vehicles (PEVs), renewable (solar) power generation resources, and fixed battery energy storage in the buildings. The reliability and availability of the electricity supply caused by severe weather elements are affecting utility customers with such integrated facilities. The proposed management approach allows such a facility to be coordinated to mitigate the potential impact of weather condition on customers electricity supply, and to provide warnings for the customers and utilities to prepare for the potential electricity supply loss. The risk assessment framework can be used to estimate and mitigate such impacts. With proper control of photovoltaic (PV) generation, PEVs with mobile battery storage and fixed energy storage, customers’ electricity demand could be potentially more flexible, since they can choose to charge the vehicles when the grid load demand is light, and stop charging or even supply energy back to the grid or buildings when the grid load demand is high. The PV generation capacity can be used to charge the PEVs, fixed battery energy storage system (BESS) or supply power to the grid. Such increased demand flexibility can enable the demand response providers with more options to respond to electricity price changes. The charging stations integration and interfacing can be optimized to minimize the operational cost or support several utility applications

Smart Grid Control: Demand Side Management in Household Refrigerators as a tool for Load Shifting

With improved supply of renewable sources of energy the focus has shifted away from simply producing clean energy to efficient consumption of energy. Until cheaper methods of energy storage are developed, Demand Side Management (DSM) is the best option for maximising energy efficiency. This paper proposes a method of turning regular refrigerators into smart demand response fridges. First, we develop an algorithm that accounts for small fluctuations in price and switches the device for optimal performance and lowered running cost. Then, we use longer price fluctuations to predict suitable times for pre-cooling and investigate the reduction in price as a result. Finally, the two models are compared, evaluated and improvements are proposed

Model Predictive Control for Building Active Demand Response Systems

The Active Demand Response (ADR), integrated with the distributed energy generation and storage systems, is the most common strategy for the optimization of energy consumption and indoor comfort in buildings, considering the energy availability and the balancing of the energy production from renewable sources. In the paper an overview of basic requirements and applications of ADR management is presented. Specifically, the model predictive control (MPC) adopted in several applications as optimal control strategy in the ADR buildings context is analysed. Finally the research experience of the authors in this context is described