Local Energy Management System Research Articles

Distributed Optimization Framework for Energy Management of Multiple Smart Homes With Distributed Energy Resources

This paper proposes a distributed optimization algorithm for scheduling the energy consumption of multiple smart homes with distributed energy resources. In the proposed approach, the centralized optimization problem for home energy management is decomposed into a two-level optimization problem, corresponding to the local home energy management system (LHEMS) at the first level and the global home energy management system (GHEMS) at the second level. The controllable household appliances (e.g., air conditioner and washing machine) are scheduled in the LHEMS within the consumer’s preferred appliance scheduling and comfort level, while the energy storage system and power trading between households are scheduled in the GHEMS. In the simulation study, the proposed distributed algorithm shows almost equivalent performance to the centralized algorithm in terms of the electricity cost and the consumer’s comfort level. The impact of different network topologies on the proposed algorithm is also analyzed, and the result provides insight into the selection of the optimal network configuration in view of the consumer’s electricity cost saving.

Global energy management system for cooperative networked residential green buildings

This study addresses an optimisation problem faced by a network of green buildings (NGBs) connected to the main electrical grid. The problem is formulated as a cooperative internal power control among interacting residential buildings. The cooperation is reached through a communication infrastructure in the NGB, where the global central controller of the network is the responsible for the coordination of the local residential buildings' energy management systems by properly allowing the optimal management of the internal and external power flows in each building. The main advantage of the cooperation among residential buildings is to better match the load profile of each building internally (at the network level). In order to achieve this goal, components such as energy storage system, distributed generations and loads are included. The uncertainties characteristics of wind speed, solar irradiation, and loads are also considered for the control and operation of the whole system. A small network of five residential buildings has been simulated using the proposed model. Numerical results demonstrate the effectiveness of the proposed network.

Advanced building energy management based on a two-stage receding horizon optimization

The continuous increase in energy demand and the integration of a large number of renewable energy sources with intermittent production require an advanced energy management to guarantee an uninterrupted supply. Deployment of smart meters in wide areas of power networks and availability of affordable storage systems encourage development and implementation of local energy management systems. Local energy management is considered a feasible and inexpensive approach to reduce the impact of load variations and intermittent generation. Demand response and load shaping techniques provide customers with the facility to contribute to system balancing and improve power quality. This paper presents a building energy management which determines the optimal scheduling of the components of the local energy system. The developed two-stage optimization is based on a receding horizon strategy and minimizes the energy costs under consideration of the physical system constraints. The proposed building energy management has been implemented and tested with a medium-size hotel emulated in a microgrid testbed using power-hardware-in-the-loop techniques. The obtained results underline that energy management strategies can be validated under realistic conditions using flexible power scenarios.

Near real time load shifting control for residential electricity prosumers under designed and market indexed pricing models

This paper presents an event driven model predictive control approach for a local energy management system, enabling residential consumers to the automated participation in demand side management (DSM) programs. We consider a household equipped with smart appliances, a storage unit, electric vehicles and photovoltaic micro-generation. Resources are coordinated according to the needs of maximizing self-consumption and minimizing the cost of energy consumption, in a contractual scenario characterized by designed or market indexed pricing models, with DSM options. The control action (appliances’ start times, the storage charging profile and the IEC 61851 compliant charging profile of the electric vehicles) is updated every time an event triggers the controller, such as a user request, a price/volume signal or the notification of a new forecast of micro-generation from the photovoltaic unit. The control framework is flexible enough to meet the real dynamics of a household and short-term grid requirements, while taking into account user preferences, contractual and technical constraints. A proper set of simulations validates the proposed approach.

Two-stage Optimization for Building Energy Management

Continuous increase of energy demand on one hand and integration of large number of renewable energy sources on the other require advanced control strategies to provide an uninterrupted service and guarantee high energy efficiency. Utilities and transmission system operators permanently supervise production facilities and grids to compensate any mismatch between production and consumption. With the gradual change from a centralized system with a few large power plants to a decentralized and distributed generation based on many renewable energy sources, local energy management systems can contribute to grid balancing. This paper presents a building energy management which determines the optimal scheduling of the different components of the local energy system. The two-stage optimization is based on the minimization of an economic function subject to the physical system constraints and uses a receding horizon approach. The proposed building energy management is implemented using mixed integer linear programming and applied in simulation to a hotel with photovoltaic installation and battery system.

Dynamic Model Predictive-Based Energy Management of DG Integrated Distribution Systems

This paper presents a new dynamic control strategy to control active and reactive power in distributed-generation integrated distribution systems. The distribution system comprises several microgrids that have a local energy-management system. The proposed method improves voltage and frequency profile in the distribution system by applying control action ahead of time. Moreover, the proposed method can operate with minimal topology information of the microgrid as it directly balances generation and consumption of power in the microgrid. Another advantage of the proposed method is that it can use fast and expensive sources, such as gas turbine generators, to balance power during transients and let slower and cheaper generators gradually take over after the transients are damped out. The proposed method can be implemented online. Therefore, it can efficiently use the time-variant reactive capabilities of the DGs to compensate reactive power needs of the system.

Intelligent Energy Optimization for User Intelligible Goals in Smart Home Environments

Intelligent management of energy consumption is one of the key issues for future energy distribution systems, smart buildings, and consumer appliances. The problem can be tackled both from the point of view of the utility provider, with the intelligence embedded in the smart grid, or from the point of view of the consumer, thanks to suitable local energy management systems (EMS). Conserving energy, however, should respect the user requirements regarding the desired state of the environment, therefore an EMS should constantly and intelligently find the balance between user requirements and energy saving. The paper proposes a solution to this problem, based on explicit high-level modeling of user intentions and automatic control of device states through the solution and optimization of a constrained Boolean satisfiability problem. The proposed approach has been integrated into a smart environment framework, and promising preliminary results are reported.

An event driven Smart Home Controller enabling consumer economic saving and automated Demand Side Management

This paper proposes the design of a Smart Home Controller strategy providing efficient management of electric energy in a domestic environment. The problem is formalized as an event driven binary linear programming problem, the output of which specifies the best time to run of smart household appliances, under a virtual power threshold constraint, taking into account the real power threshold and the forecast of consumption from not plannable loads. The optimization is performed each time the system is triggered by proper events, in order to tailor the controller action to the real life dynamics of an household. This problem formulation allows to analyze relevant scenarios from consumer and energy retailer point of view: here overload management, optimization of economic saving in case of Time of Use Tariff and Demand Side Management have been discussed and simulated. Simulations have been performed on relevant test cases, based on real load profiles provided by the smart appliance manufacturer Electrolux S.p.A. and on energy tariffs suggested by the energy retailer Edison. Results provide a proof of concept about the consumers benefits coming from the use of local energy management systems and the relevance of automated Demand Side Management for the general target of efficient and cost effective operation of electric networks.