There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Use of model predictive control and weather forecasts for energy efficient building climate control

A communication infrastructure is an essential part to the success of the emerging smart grid. A scalable and pervasive communication infrastructure is crucial in both construction and operation of a smart grid. In this paper, we present the background and motivation of communication infrastructures in smart grid systems. We also summarize major requirements that smart grid communications must meet. From the experience of several industrial trials on smart grid with communication infrastructures, we expect that the traditional carbon fuel based power plants can cooperate with emerging distributed renewable energy such as wind, solar, etc, to reduce the carbon fuel consumption and consequent green house gas such as carbon dioxide emission. The consumers can minimize their expense on energy by adjusting their intelligent home appliance operations to avoid the peak hours and utilize the renewable energy instead. We further explore the challenges for a communication infrastructure as the part of a complex smart grid system. Since a smart grid system might have over millions of consumers and devices, the demand of its reliability and security is extremely critical. Through a communication infrastructure, a smart grid can improve power reliability and quality to eliminate electricity blackout. Security is a challenging issue since the on-going smart grid systems facing increasing vulnerabilities as more and more automation, remote monitoring/controlling and supervision entities are interconnected.

https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1315&context=electricalengineeringfacpub

A Survey on Smart Grid CommunicationInfrastructures: Motivations, Requirements andChallenges

Theory and applications of HVAC control systems – A review of model predictive control (MPC)

The significant benefits associated with microgrids have led to vast efforts to expand their penetration in electric power systems. Although their deployment is rapidly growing, there are still many challenges to efficiently design, control, and operate microgrids when connected to the grid, and also when in islanded mode, where extensive research activities are underway to tackle these issues. It is necessary to have an across-the-board view of the microgrid integration in power systems. This paper presents a review of issues concerning microgrids and provides an account of research in areas related to microgrids, including distributed generation, microgrid value propositions, applications of power electronics, economic issues, microgrid operation and control, microgrid clusters, and protection and communications issues.

State of the Art in Research on Microgrids: A Review

Microgrids are subsystems of the distribution grid which comprises small generation capacities, storage devices and controllable loads, which can operate either connected or isolated from the utility grid. This paper studies the microgrid economic scheduling, i.e. the problem of optimize microgrid operations to fulfil a time-varying energy demand and operational constraints while minimizing the costs of internal production and imported energy from the utility grid. The problem is posed as a mixed-integer linear programming model. The key difference in the proposed modeling approach is that no complex heuristics or decompositions are used; the full model is formulated and solved in an efficient way by using commercial solvers. This leads to significant improvements in schedule quality and in computational burden. A case study of a typical microgrid is investigated: simulation results show the feasibility and the effectiveness of the proposed approach.

A mixed integer linear formulation for microgrid economic scheduling

This paper presents an investigation of how Model Predictive Control (MPC) and weather predictions can increase the energy efficiency in Integrated Room Automation (IRA) while respecting occupant comfort. IRA deals with the simultaneous control of heating, ventilation and air conditioning as well as blind positioning and electric lighting such that the room temperature as well as CO2 and luminance levels stay within given comfort ranges. MPC is an advanced control technique which, when applied to buildings, employs a model of the building dynamics and solves an optimization problem to determine the optimal control inputs. The result is an optimal plan in the sense that it takes into account the future weather and internal gains and controls the HVAC, light and blind units to minimize energy costs while respecting comfort constraints. Through a large-scale factorial simulation study we show that MPC coupled with weather predictions is beneficial in terms of energy efficiency and occupant comfort. In particular, we investigate the control performance, the impact of the accuracy of weather predictions as well as the robustness and tunability of the control strategy.

Increasing energy efficiency in building climate control using weather forecasts and model predictive control

Microgrids are subsystems of the distribution grid which comprises generation capacities, storage devices and controllable loads, which can operate either connected or isolated from the utility grid. In this work, microgrid management system is developed in a stochastic framework. Uncertainties due to fluctuating demand and generation from renewable energy sources are taken into account and a two-stage stochastic programming approach is applied to efficiently optimize microgrid operations while satisfying a time-varying request and operation constraints. Mathematically, the stochastic optimization problem is stated as a mixed-integer linear programming problem, which can be solved in an efficient way by using commercial solvers. The stochastic problem is incorporated in a Model Predictive Control (MPC) scheme to further compensate the uncertainty though the feedback mechanism. Simulations show the effective performance of the proposed approach.

/pdf/stochastic-model-predictive-control-for-economic-11i8aezdc8.pdf

Stochastic Model Predictive Control for economic/environmental operation management of microgrids

This paper describes an employee scheduling system for retail outlets; it is a constraint-based system that exploits forecasts and stochastic techniques to generate schedules meeting the demand for sales personnel. Uncertain scenarios due to fluctuating demand are taken into account to develop a stochastic operational optimization of staffing levels. Mathematically, the problem is stated as a mixed-integer linear programming problem. Simulations with store data belonging to a major Swiss retailer show the effective performance of the proposed approach. The schedule quality is assessed through comparison with a deterministic scheduling package, which has been used at several outlets in Switzerland.

A two-stage stochastic programming approach to employee scheduling in retail outlets with uncertain demand

In this paper, we propose a robust approach for scheduling of smart appliances and electrical energy storages (EESs) in active apartments with the aim of reducing both the electricity bill and the $\hbox{CO}_{2}$ emissions. The proposed robust formulation takes the user behavior uncertainty into account so that the optimal appliances schedule is less sensitive to unpredictable changes in user preferences. The user behavior uncertainty is modeled as uncertainty in the cost function coefficients. In order to reduce the level of conservativeness of the robust solution, we introduce a parameter allowing to achieve a trade-off between the price of robustness and the protection against uncertainty. Mathematically, the robust scheduling problem is posed as a multi-objective Mixed Integer Linear Programming (MILP), which is solved by using standard algorithms. The numerical results show effectiveness of the proposed approach to increase both the electricity bill and $\hbox{CO}_{2}$ emissions savings, in the presence of user behavior uncertainties. Mathematical insights into the robust formulation are illustrated and the sensitivity of the optimum cost in the presence of uncertainties is investigated. Although home appliances and EESs are considered in this work, we point out that the proposed scheduling framework is generally applicable to many use cases, e.g., charging and discharging of electrical vehicles in an effective way. In addition, it is applicable to various scenarios considering different uncertainty sources, different storage technologies and generic programmable electrical loads, as well as different optimization criteria.

Alessandra Parisio

Papers

A mixed integer linear formulation for microgrid economic scheduling

Increasing energy efficiency in building climate control using weather forecasts and model predictive control

Stochastic Model Predictive Control for economic/environmental operation management of microgrids

A two-stage stochastic programming approach to employee scheduling in retail outlets with uncertain demand

Robust Scheduling of Smart Appliances in Active Apartments With User Behavior Uncertainty