Long-term experimental study of price responsive predictive control in a real occupied single-family house with heat pump

The continuous introduction of renewable electricity and increased consumption through electrification of the transport and heating sector challenges grid stability. This study investigates load shifting through demand side management as a solution. We present a four-month experimental study of a low-complexity, hierarchical Model Predictive Control approach for demand side management in a near-zero emission occupied single-family house in Denmark. The control algorithm uses a price signal, weather forecast, a single-zone building model, and a non-linear heat pump efficiency model to generate a space-heating schedule. The weather-compensated, commercial heat pump is made to act smart grid-ready through outdoor temperature input override to enable heat boosting and forced stops to accommodate the heating schedule. The cost reduction from the controller ranged from 2-33% depending on the chosen comfort level. The experiment demonstrates that load shifting is feasible and cost-effective, even without energy storage, and that the current price scheme provides an incentive for Danish end-consumers to shift heating loads. However, issues related to controlling the heat pump through input-manipulation were identified, and the authors propose a more promising path forward involving coordination with manufacturers and regulators to make commercial heat pumps truly smart grid-ready

Computational intelligence approaches for energy load forecasting in smart energy management grids: state of the art, future challenges, and research directions and Research Directions

Energy management systems are designed to monitor, optimize, and control the smart grid energy market. Demand-side management, considered as an essential part of the energy management system, can enable utility market operators to make better management decisions for energy trading between consumers and the operator. In this system, a priori knowledge about the energy load pattern can help reshape the load and cut the energy demand curve, thus allowing a better management and distribution of the energy in smart grid energy systems. Designing a computationally intelligent load forecasting (ILF) system is often a primary goal of energy demand management. This study explores the state of the art of computationally intelligent (i.e., machine learning) methods that are applied in load forecasting in terms of their classification and evaluation for sustainable operation of the overall energy management system. More than 50 research papers related to the subject identified in existing literature are classified into two categories: namely the single and the hybrid computational intelligence (CI)-based load forecasting technique. The advantages and disadvantages of each individual techniques also discussed to encapsulate them into the perspective into the energy management research. The identified methods have been further investigated by a qualitative analysis based on the accuracy of the prediction, which confirms the dominance of hybrid forecasting methods, which are often applied as metaheurstic algorithms considering the different optimization techniques over single model approaches. Based on extensive surveys, the review paper predicts a continuous future expansion of such literature on different CI approaches and their optimizations with both heuristic and metaheuristic methods used for energy load forecasting and their potential utilization in real-time smart energy management grids to address future challenges in energy demand managemen

SALSA: A Formal Hierarchical Optimization Framework for Smart Grid

The smart grid, by the integration of advanced control and optimization technologies, provides the traditional grid with an indisputable opportunity to deliver and utilize the electricity more efficiently. Building smart grid applications is a challenging task, which requires a formal modeling, integration, and validation framework for various smart grid domains. The design flow of such applications must adapt to the grid requirements and ensure the security of supply and demand. This dissertation, by proposing a formal framework for customers and operations domains in the smart grid, aims at delivering a smooth way for: i) formalizing their interactions and functionalities, ii) upgrading their components independently, and iii) evaluating their performance quantitatively and qualitatively.The framework follows an event-driven demand response program taking no historical data and forecasting service into account. A scalable neighborhood of prosumers (inside the customers domain), which are equipped with smart appliances, photovoltaics, and battery energy storage systems, are considered. They individually schedule their appliances and sell/purchase their surplus/demand to/from the grid with the purposes of maximizing their comfort and profit at each instant of time. To orchestrate such trade relations, a bilateral multi-issue negotiation approach between a virtual power plant (on behalf of prosumers) and an aggregator (inside the operations domain) in a non-cooperative environment is employed. The aggregator, with the objectives of maximizing its profit and minimizing the grid purchase, intends to match prosumers' supply with demand. As a result, this framework particularly addresses the challenges of: i) scalable and hierarchical load demand scheduling, and ii) the match between the large penetration of renewable energy sources being produced and consumed. It is comprised of two generic multi-objective mixed integer nonlinear programming models for prosumers and the aggregator. These models support different scheduling mechanisms and electricity consumption threshold policies.The effectiveness of the framework is evaluated through various case studies based on economic and environmental assessment metrics. An interactive web service for the framework has also been developed and demonstrated

Integrated Transmission and Distribution Control

Distributed, generation, demand response, distributed storage, smart appliances, electric vehicles and renewable energy resources are expected to play a key part in the transformation of the American power system. Control, coordination and compensation of these smart grid assets are inherently interlinked. Advanced control strategies to warrant large-scale penetration of distributed smart grid assets do not currently exist. While many of the smart grid technologies proposed involve assets being deployed at the distribution level, most of the significant benefits accrue at the transmission level. The development of advanced smart grid simulation tools, such as GridLAB-D, has led to a dramatic improvement in the models of smart grid assets available for design and evaluation of smart grid technology. However, one of the main challenges to quantifying the benefits of smart grid assets at the transmission level is the lack of tools and framework for integrating transmission and distribution technologies into a single simulation environment. Furthermore, given the size and complexity of the distribution system, it is crucial to be able to represent the behavior of distributed smart grid assets using reduced-order controllable models and to analyze their impacts on the bulk power system in terms of stability and reliability. The objectives of the project were to: • Develop a simulation environment for integrating transmission and distribution control, • Construct reduced-order controllable models for smart grid assets at the distribution level, • Design and validate closed-loop control strategies for distributed smart grid assets, and • Demonstrate impact of integrating thousands of smart grid assets under closed-loop control demand response strategies on the transmission system. More specifically, GridLAB-D, a distribution system tool, and PowerWorld, a transmission planning tool, are integrated into a single simulation environment. The integrated environment allows the load flow interactions between the bulk power system and end-use loads to be explicitly modeled. Power system interactions are modeled down to time intervals as short as 1-second. Another practical issue is that the size and complexity of typical distribution systems makes direct integration with transmission models computationally intractable. Hence, the focus of the next main task is to develop reduced-order controllable models for some of the smart grid assets. In particular, HVAC units, which are a type of Thermostatically Controlled Loads (TCLs), are considered. The reduced-order modeling approach can be extended to other smart grid assets, like water heaters, PVs and PHEVs. Closed-loop control strategies are designed for a population of HVAC units under realistic conditions. The proposed load controller is fully responsive and achieves the control objective without sacrificing the end-use performance. Finally, using the T&D simulation platform, the benefits to the bulk power system are demonstrated by controlling smart grid assets under different demand response closed-loop control strategies

Exploiting V2G to optimize residential energy consumption with electrical vehicle (dis)charging

Abstract-The potential breakthrough of pluggable (hybrid) electrical vehicles (PHEVs) will impose various challenges to the power grid, and esp. implies a significant increase of its load. Adequately dealing with such PHEVs is one of the challenges and opportunities for smart grids. In particular, intelligent control strategies for the charging process can significantly alleviate peak load increases that are to be expected from e.g. residential vehicle charging at home. In addition, the car batteries connected to the grid can also be exploited to deliver grid services, and in particular give stored energy back to the grid to help coping with peak demands stemming from e.g. household appliances. In this paper, we will address such so-called vehicle-to-grid (V2G) scenarios while considering the optimization of PHEV charging in a residential scenario. In particular, we will assess the optimal car battery (dis)charging scheduling to achieve peak shaving and reduction of the variability (over time) of the load of households connected to a local distribution grid. We compare (i) a business-as-usual (BAU) scenario, without any intelligent charging, (ii) intelligent local charging optimization without V2G, and (iii) charging optimization with V2G. To evaluate these scenarios, we make use of our simulation tool, based on OMNeT++, which combines ICT and power network models and incorporates a Matlab model that allows e.g. assessing voltage violations. In a case study on a threefeeder distribution network spanning 63 households, we observe that non-V2G optimized charging can reduce the peak demand compared to BAU with 64%. If we apply V2G to the intelligent charging, we can further cut the non-V2G peak demand with 17% (i.e., achieve a peak load which is only 30% of BAU)

Utilization of Battery Energy Storage Systems (BESS) in Smart Grid: A Review

The uncertainty in fuel cost, the ageing of most existing grid, the lack of utilities’ supply capacity to respond to the increasing load demand, and the lack of automatically power restoration, accelerate the need to modernize the distribution network by introducing new technologies, putting the smart grid (SG) on spot. The aim of this paper is to carry out a detailed survey of the major requirements of (SG) and discuss the operational challenges arising from the integration of distributed generation (DG) in distribution networks (DN). These requirements dictate the necessity to review the energy and communication infrastructure, the automatic control, metering and monitoring systems, and highlight the features of smart protection system for a robust and efficient distribution grid. In addition, the paper aims to classify the energy storage systems (ESS) and explain their role for utilities, consumers and for environment. This includes the pumped hydro systems (PHS) and compressed air systems (CAS), battery energy storage systems (BESSs), double layer and superconductive capacitors, and electric vehicles (EVs). Since BESSs emerged as one of the most promising technology for several power applications, the paper presents an overview of their main features, management and control systems and operational modes. A survey about the utilization of BESSs in power system is presented

Cyber Physical Energy Systems Modules for Power Sharing Controllers in Inverter Based Microgrids

The Microgrids (MGs) are an effective way to deal with the smart grid challenges, including service continuity in the event of a grid interruption, and renewable energy integration. The MGs are compounded by multiple distributed generators (DGs), and the main control goals are load demand sharing and voltage and frequency stability. Important research has been reported to cope with the implementation challenges of the MGs including the power sharing control problem, where the use of cybernetic components such as virtual components, and communication systems is a common characteristic. The use of these cybernetic components to control complex physical systems generates new modeling challenges in order to achieve an adequate balance between complexity and accuracy in the MG model. The standardization problem of the cyber-physical MG models is addressed in this work, using a cyber-physical energy systems (CPES) modeling methodology to build integrated modules, and define the communication architectures that each power sharing control strategy requires in an AC-MG. Based on these modules, the control designer can identify the signals and components that eventually require a time delay analysis, communication requirements evaluation, and cyber-attacks’ prevention strategies. Similarly, the modules of each strategy allow for analyzing the potential advantages and drawbacks of each power sharing control technique from a cyber physical perspective

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

Assessment of novel distributed control techniques to address network constraints with demand side management

The development of sustainable generation, a reliable electricity supply and affordable tariffs are the primary requirements to address the uncertainties in different future energy scenarios. Due to the predicted increase in Distributed Generation (DG) and load profile changes in future scenarios, there are significant operational and planning challenges facing netwrok operators. These changes in the power system distribution network require a new Active Network Management (ANM) control system to manage distribution constraint issues such as thermal rating, voltage, and fault levels. The future smart grid focuses on harnessing the control potential from demand side via bidirectional power flow, transparent information communication, and contractual customer participation. Demand Side Management (DSM) is considered as one of the effective solutions to defer network capacity reinforcement, increase energy efficiency, facilitate renewable access, and implement low carbon energy strategy. From the Distribution Network Operator's (DNO) perspective, the control opportunity from Demand Response (DR) and Decentralized Energy Resource (DER) contributes on capacity investment reduction, energy efficiency, and enable low carbon technologies. This thesis develops a new decentralized control system for dealing effectively with the constraint issues in the Medium Voltage (MV) distribution network. In the decentralized control system, two novel control approaches are proposed to autonomously relieve the network thermal constraint via DNO's direct control of the real power in network components during the operation period. The first approach, Demand Response for Power Flow Management (DR-PFM), implements the DSM peak clipping control of Active Demand (AD), whilst the second approach, Hybrid Control for Power Flow Management (HC-PFM), implements the hybrid control of both AD and DER. The novelty of these two new control algorithms consists in the application of a Constraint Satisfaction Problem (CSP) based programming model on decision making of the real power curtailment to relieve the network thermal overload. In the Constraint Programming (CP) model, three constraints are identified: a preference constraint, and a network constraint. The control approaches effectively solve the above constraint problem in the CSP model within 5 seconds' time response. The control performance is influenced by the pre-determined variable, domain and constraint settings. These novel control approaches take advantages on flexible control, fast response and demand participation enabling in the future smart grid.The development of sustainable generation, a reliable electricity supply and affordable tariffs are the primary requirements to address the uncertainties in different future energy scenarios. Due to the predicted increase in Distributed Generation (DG) and load profile changes in future scenarios, there are significant operational and planning challenges facing netwrok operators. These changes in the power system distribution network require a new Active Network Management (ANM) control system to manage distribution constraint issues such as thermal rating, voltage, and fault levels. The future smart grid focuses on harnessing the control potential from demand side via bidirectional power flow, transparent information communication, and contractual customer participation. Demand Side Management (DSM) is considered as one of the effective solutions to defer network capacity reinforcement, increase energy efficiency, facilitate renewable access, and implement low carbon energy strategy. From the Distribution Network Operator's (DNO) perspective, the control opportunity from Demand Response (DR) and Decentralized Energy Resource (DER) contributes on capacity investment reduction, energy efficiency, and enable low carbon technologies. This thesis develops a new decentralized control system for dealing effectively with the constraint issues in the Medium Voltage (MV) distribution network. In the decentralized control system, two novel control approaches are proposed to autonomously relieve the network thermal constraint via DNO's direct control of the real power in network components during the operation period. The first approach, Demand Response for Power Flow Management (DR-PFM), implements the DSM peak clipping control of Active Demand (AD), whilst the second approach, Hybrid Control for Power Flow Management (HC-PFM), implements the hybrid control of both AD and DER. The novelty of these two new control algorithms consists in the application of a Constraint Satisfaction Problem (CSP) based programming model on decision making of the real power curtailment to relieve the network thermal overload. In the Constraint Programming (CP) model, three constraints are identified: a preference constraint, and a network constraint. The control approaches effectively solve the above constraint problem in the CSP model within 5 seconds' time response. The control performance is influenced by the pre-determined variable, domain and constraint settings. These novel control approaches take advantages on flexible control, fast response and demand participation enabling in the future smart grid