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State of the art review on model predictive control (MPC) in Heating Ventilation and Air-conditioning (HVAC) field

Time-shiftable loads have recently received an increasing attention due to their role in creating load flexibility and enhancing demand response and peak-load shaving programs. In this paper, we seek to answer the following question: How can a time-shiftable load, that itself may comprise of several smaller time-shiftable subloads, submit its demand bids to the day-ahead and real-time markets so as to minimize its energy procurement cost? Answering this question is challenging because of the inter-temporal dependencies in choosing the demand bids for time-shiftable loads, and also due to the coupling between demand bid selection and time-shiftable load scheduling problems. Nevertheless, we answer the above question for different practical bidding scenarios and based on different statistical characteristics of practical market prices. In all cases, closed form solutions are obtained for the optimal choices of the price and energy bids. The bidding performance is then evaluated in details by examining several case studies and analyzing actual market price data.

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Optimal demand bidding for time-shiftable loads

Residential loads, especially heating, ventilation and air conditioners (HVACs) and electric vehicles (EVs), have great potentials to provide demand flexibility which is an attribute of grid-interactive efficient buildings (GEB). Under this new paradigm, EV and HVAC aggregator models are first developed in this paper to represent the fleet of GEBs, in which the aggregated parameters are obtained based on a new approach of data generation and least squares parameter estimation (DG-LSPE), which can deal with heterogeneous HVACs. Then, a tri-level bidding and dispatching framework is established based on competitive distribution operation with distribution locational marginal price (DLMP). The first two levels form a bilevel model to optimize the aggregators’ payment and to represent the interdependency between load aggregators and the distribution system operator (DSO) using DLMP, and the third level is to dispatch the optimal load aggregation to all residents by the proposed priority list-based demand dispatching algorithm. Finally, case studies on a modified IEEE 33-Bus system illustrate three main technical reasons of payment reduction due to demand flexibility: load shifts, DLMP step changes, and power losses. They can be used as general guidelines for better decision-making for future planning and operation of demand response programs.

Tri-Level Scheduling Model Considering Residential Demand Flexibility of Aggregated HVACs and EVs Under Distribution LMP

The need to optimize the energy consumption of commercial buildings– responsible for over 40% of U.S. energy consumption–has recently gained significant attention due to the call for energy efficiency. Moreover, the ability to participate in the retail electricity markets through proactive demand-side participation has recently led to the development of an economic model predictive control (EMPC) for these buildings’ Heating, Ventilation, and Air Conditioning (HVAC) systems. The objective of this paper is to develop a price-responsive operational model for buildings’ HVAC systems while considering inflexible loads and other distributed energy resources (DERs), including photovoltaic (PV) generation and battery storage systems. A Nonlinear Economic Model Predictive Controller (NL-EMPC) is presented to minimize the net cost of energy usage by the buildings’ flexible loads, i.e., HVAC systems while satisfying the comfort-level of buildings’ occupants. To improve the computational efficiency of the HVAC system controller, we propose a linearized economic model predictive controller (L-EMPC). The L-EMPC is a novel approximate linearized model for the NL-EMPC and is based on the feedback linearization technique. The proposed approach results in a controller for the building with the reduced complexity that accurately approximates the original nonlinear plant dynamics with its economic constraints. The efficiency of the proposed EMPC controllers are evaluated using several simulation case studies.

Linearized Price-Responsive HVAC Controller for Optimal Scheduling of Smart Building Loads

The increasing penetration of proactive agents in electric power distribution systems calls for mechanisms to coordinate their trading activities. The commonly employed centralized and distributed methods for supply-demand coordination are not only limited by hardware requirements but also restrictive for active participation of the market agents. This paper proposes a novel decentralized transactive coordination framework that allows participants to bid and clear transactions via a bilateral interaction process. The framework is based on 1) power consumption flexibility individually reported by demand agents using model-predictive control, 2) bilateral supply-side bidding individually determined by the supply agents using a Markowitz Portfolio Optimization model, and 3) individual simultaneous clearing of transactions by demand agents based on second-price sealed-bid auctions. The suppliers bid in the market to achieve desired returns while minimizing their risks. The demand agents harness their demand flexibility by competitively clearing the supplier bids. Unlike traditional approaches, the proposed framework does not require master nodes to orchestrate transactions and, therefore, facilitates massive participation of market agents. Simulation cases demonstrate that the proposed framework successfully performs the market function for bilateral transactions among thousands of proactive agents.

Decentralized Transactive Energy for Flexible Resources in Distribution Systems

Owing to the call for energy efficiency, the need to optimize the energy consumption of commercial buildings–responsible for over 40% of US energy consumption–has recently gained significant attention. Moreover, the ability to participate in the retail electricity markets through proactive demand-side participation has recently led to development of economic model predictive control (EMPC) for building's Heating, Ventilation, and Air Conditioning (HVAC) system. The objective of this paper is to develop a price-sensitive operational model for building's HVAC systems while considering inflexible loads and other distributed energy resources (DERs) such as photovoltaic (PV) generation and battery storage for the buildings. A Nonlinear Economic Model Predictive Controller (NL-EMPC) is presented to minimize the net cost of energy usage by building's HVAC system while satisfying the comfort-level of building's occupants. The efficiency of the proposed NL-EMPC controller is evaluated using several simulation case studies.

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Smart Building Energy Management using Nonlinear Economic Model Predictive Control

The proactive control of building’s heating, ventilation, and air conditioning (HVAC) system can reduce energy consumption and provides cost saving. An efficient design of a controller for a complex system such as a building, which is also prone to uncertainties and disturbances, calls for advanced control methods such as model predictive control (MPC). This article presents a tube-based MPC to minimize the net cost of energy usage by the building’s HVAC system while satisfying the comfort level of the building’s occupants. Contrary to the conventional MPC, this controller is robust against model uncertainty and exogenous disturbances affecting the building thermal load model. Compared to other robust approaches such as the min–max controller, the proposed controller is of lower computational complexity and can be used with the environment having complex models, e.g., with nonlinear dynamics. Moreover, the proposed controller shows a satisfactory performance when optimizing an economic objective, such as cost minimization. The merits of using the proposed MPC controller are demonstrated using several simulation cases.

Tube-Based Model Predictive Controller for Building’s Heating Ventilation and Air Conditioning (HVAC) System

The active participation of demand response (DR) resources into the wholesale market price formation and load dispatch process has the potential to stimulate demand-side flexibility. However, it is challenging for a market entity to utilize the DR resources for practical use. This is because day-ahead wholesale market-clearing prices are uncertain, and DR resources are heterogeneous. Furthermore, DR participation may lead to violations of the distribution system’s operational constraints. In this article, we propose an approach for an aggregator/load-serving entity (LSE) to profitably bid aggregated DR resources into the day-ahead wholesale market. The LSE requires an optimal bidding strategy that reflects the price elasticity of the aggregated retail loads to participate in the wholesale market operations. In the proposed approach, the LSE executes load curtailment and load shifting contracts with DR resources, where DR resources are remunerated for their participation at pre-contracted incentive prices. Then, the LSE aggregates the DR flexibility and optimally bids it in the day-ahead wholesale market. The proposed approach is validated using the IEEE-123-bus test system. It is demonstrated that the LSE can successfully generate economic bids for its participation in the day-ahead market by optimal management of DR resources and without violating the network’s operating constraints.

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Mohammad Ostadijafari

Papers

Linearized Price-Responsive HVAC Controller for Optimal Scheduling of Smart Building Loads

Decentralized Transactive Energy for Flexible Resources in Distribution Systems

Smart Building Energy Management using Nonlinear Economic Model Predictive Control

Tube-Based Model Predictive Controller for Building’s Heating Ventilation and Air Conditioning (HVAC) System

Demand-Side Participation via Economic Bidding of Responsive Loads and Local Energy Resources