Yi Zong

Application genetic algorithms for load management in refrigerated warehouses with wind power penetration

Wind energy is produced at random times, whereas the energy consumption pattern shows distinct demand peaks during day-time and low levels during the night. The use of a refrigerated warehouse as a giant battery for wind energy is a new possibility that is being studied for wind energy integration as well as a way to store electricity produced during night-time by wind turbines. The controller for load management in a refrigerated warehouse with wind power penetration by GA-based is introduced in this paper. The objective function is to minimize the energy consumption for operating the refrigerated warehouse. It can be seen that the GA-based control strategy achieves feasible results for operating the temperature in refrigerated warehouse. Balancing the wind power production with refrigerated warehouse load management promises to be a clean and cost effective method. For refrigerated warehouse owners, it has the potential to lower operational costs.

Nonlinear economic model predictive control strategy for active smart buildings

Nowadays, the development of advanced and innovative intelligent control techniques for energy management in buildings is a key issue within the smart grid topic. A nonlinear economic model predictive control (EMPC) scheme, based on the branch-and-bound tree search used as optimization algorithm for solving the nonconvex optimization problem is proposed in this paper. A simulation using the nonlinear model-based controller to control the temperature levels of an intelligent office building (PowerFlexHouse) is addressed. Its performance is compared with a linear model-based controller. The nonlinear controller is shown very reliable keeping the comfort levels in the two considered seasons and shifting the load away from peak hours in order to achieve the desired flexible electricity consumption.

Energylab Nordhavn: An integrated community energy system towards green heating and e-mobility

This paper analyzes the green potential of a newly developed urban community, i.e., Nordhavn, in Copenhagen, Denmark from a planning perspective, wherein the energy sector of power, heat and transportation will be developed as an integrated energy system solution. Based on an hour-by-hour analysis wherein the generation and demand in each energy sector are balanced, the analysis explains how different levels of penetration of centralized heat pumps (HPs) and electric vehicles (EVs) would influence the energy performance of this integrated community energy system. The performance of the integrated energy system is evaluated from the perspectives of annual fuel consumption, electricity import, system cost and CO2 emission, etc.

Potential Energy Flexibility for a Hot-Water Based Heating System in Smart Buildings via Economic Model Predictive Control

This paper studies the potential of shifting the heating energy consumption in a residential building to low price periods based on varying electricity price signals suing Economic Model Predictive Control strategy. The investigated heating system consists of a heat pump incorporated with a hot water tank as active thermal energy storage, where two optimization problems are integrated together to optimize both the heat pump electricity consumption and the building heating consumption. A sensitivity analysis for the system flexibility is examined. The results revealed that the proposed controller can successfully achieve significant shifting potentials compared to a baseline case.

Simulation platform developed to study and identify critical cases in a future smart grid

This paper proposes a simulation platform developed to study and identify critical cases in a Smart Grid. A distribution network with different Distributed Energy Resources (DER) components, connected along the feeders, is analyzed, having the objective to identify limitations of existing simulation and planning tools, with a particular objective on the challenges faced by the introduction of Smart Grid technologies. Another important issue of the paper is to identify critical load cases, as well as the voltage variations with the highest potential, able to implement the grid model and the worst case scenarios developed.

Fuzzy predictive filtering in nonlinear economic model predictive control for demand response

The performance of a model predictive controller (MPC) is highly correlated with the models accuracy. This paper introduces an economic model predictive control (EMPC) scheme based on a nonlinear model, which uses a branch-and-bound tree search for solving the inherent non-convex optimization problem. Moreover, to reduce the computation time and improve the controllers performance, a fuzzy predictive filter is introduced. With the purpose of testing the developed EMPC, a simulation controlling the temperature levels of an intelligent office building (PowerFlexHouse), with and without fuzzy filtering, is performed. The results show that the controller achieves a good performance while keeping the temperature inside the predefined comfort limits. Fuzzy predictive filtering has shown to be an effective tool which is capable of reducing the computational burden and increasing the performance level of the control algorithm.

Multi-agents Based Modelling for Distribution Network Operation with Electric Vehicle Integration

Electric vehicles (EV) can become integral part of a smart grid because instead of just consuming power they are capable of providing valuable services to power systems. To integrate EVs smoothly into the power systems, a multi-agents system (MAS) with hierarchical organization structure is proposed in this paper. The proposed MAS system consists of three types of agents: distribution system operator agent (DSO agent), electric vehicle fleet operator agent (EV FO agent or alternatively called virtual power plant agent) and EV agent. A DSO agent belongs to the top level of the hierarchy and its role is to manage the distribution network safely by avoiding grid congestions and using congestion prices to coordinate the energy schedule of VPPs. VPP agents belong to the middle level and their roles are to manage the charge periods of the EVs. EV agents sit in the bottom level and they represent EV owners and operate the charging behaviour of EVs. To simulate this collaborative (all agents contribute to achieving an optimized global performance) but also competitive environment (each agent will try to increase its utilities or reduce its costs), a multi-agent platform was developed to demonstrate the coordination between the interacting agents.

Optimal dispatch of battery storage in an industrial microgrid with a mixed portfolio of renewables

Local brownouts are a nuisance and have driven consumers to take greater responsibility for their electricity supply-particularly industries and communities with a critical need for reliable and safe power. In this paper, an optimal dispatch solution is developed for a battery storage system applied in a real industrial Microgrid which has a mixed portfolio of renewables. By applying a model predictive control (MPC)-based dispatch strategy with multi-period AC optimal power flow (AC-OPF) consideration, trade-offs among multiple objectives of the industrial Microgrid, such as economic operation, reduction of power feed-back and improving the voltage quality etc., are balanced. Simulations are presented to demonstrate the proposed approach and to verify its effectiveness.

Model Predictive Controller Design for a Load Management Research Facility in a Distributed Power System

Abstract This paper introduces PowerFlexHouse, a research facility for exploring the technical potential of active load management in a distributed power system with a high penetration of renewable energy. A description of the facility based on a distributed power system (SYSLAB) is followed by a discussion of the software platform on which building controllers can be executed. Finally, this paper shows how to design a thermal model predictive controller for the power consumption prediction in this distributed power system. The control of the PowerFlexHouse allows studies to be performed on the reaction of this intelligent house to a hybrid power grid. With this demand side control study, we hope we can not only dramatically improve grid reliability, but also raise energy efficiencies and reduce power cost for users.