Demand Response Operation Research Articles

Optimal demand response operation of electric boosting glass furnaces

The glass industry is highly energy-intensive, accounting for 1% of total industrial energy consumption in the United States. Most of the energy consumption in the glass manufacturing process is attributable to the significant heat required to melt raw materials. An electric boosting system which can transfer extra heat (5%–20% of total energy) to the glass melt in addition to the energy from natural gas combustion can be implemented in a glass furnace. Electric boosting is thermally efficient, reduces direct pollutant emissions, and prolongs furnace superstructure lifespan. However, a high level of electric boost is not always economically desirable, considering the volatility of electricity prices. Balancing between natural gas and electricity consumption in a demand response strategy can reduce the energy cost and mitigate strain on the electrical grid. In this paper, a physics-based model is developed to describe the dynamic behavior of a prototype electric boosting glass furnace. We present a dynamic optimization strategy to optimally balance between using natural gas and electric power under electricity price fluctuations. Case studies on the effect of varying energy prices and emissions regulations are analyzed.

A data-driven model predictive control for lighting system based on historical occupancy in an office building: Methodology development

The lighting system accounts for 8% of the total electricity consumption in commercial buildings in the United States and 12% of the total electricity consumption in public buildings globally. This consumption share can be effectively reduced using the demand-response control. The traditional lighting system control method commonly depends on the real-time occupancy data collected using the passive infrared (PIR) sensor. However, the detection inaccuracy of the PIR sensor usually results in false-offs. To diminish the false-error frequency, the existing lighting system control simply deploys a delayed reaction period (e.g., 5 to 20 min), which is not sufficiently accurate for the demand-response operation. Therefore, in this research, a novel data-driven model predictive control (MPC) method that is based on the temporal sequential-based artificial neural network (TS-ANN) is proposed to overcome this challenge using an updated historical occupancy status. Using an office as case study, the proposed model is also compared with the traditional lighting system control method. In the proposed model, the occupancy data was trained to predict the occupancy pattern to improve the control. It was found that the occupancy prediction mainly correlates with the historical occupancy ratio and the time sequential feature. The simulation results indicated that the proposed method achieved higher accuracy (97.4%) and fewer false-offs (from 79.5 with traditional time delay method to 0.6 times per day) are achieved by the MPC model. The proposed TS-ANN-MPC method integrates the analysis of the occupant behavior routine into on-site control and has the potential to further enhance the control performance practice for maximum energy conservation.

Stochastic Scheduling and Control Using Data-Driven Nonlinear Dynamic Models: Application to Demand Response Operation of a Chlor-Alkali Plant

Modern chemical manufacturers are increasingly interested in improving the agility of their operations. This is particularly true in regions with deregulated electricity markets, where participatio...

Implementation of a Demand-Side Management Solution for South Korea’s Demand Response Program

In this paper, we show the development of a demand-side management solution (DSMS) for demand response (DR) aggregator and actual demand response operation cases in South Korea. To show an experience, Korea’s demand response market outline, functions of DSMS, real contracted capacity, and payment between consumer and load aggregator and DR operation cases are revealed. The DSMS computes the customer baseline load (CBL), relative root mean squared error (RRMSE), and payments of the customers in real time. The case of 10 MW contracted customers shows 108.03% delivery rate and a benefit of 854,900,394 KRW for two years. The results illustrate that an integrated demand-side management solution contributes by participating in a DR market and gives a benefit and satisfaction to the consumer.

Demand response capacity constrained optimisation of multicast routing in smart grid

Low-latency Demand Response (DR) operation is urgently required to maintain grid stability in the context of wide area control for smart grids. Multicast-based communications are a feasible choice for reducing DR communication delay. Traditional multicast routing algorithms that only consider constraints such as bandwidth or delay will cause users with large DR capacities to need more time to perform DR operations, resulting in increased power grid frequency amplitude and adjustment time. To address this issue, a systematic approach, namely minimum Bandwidth Tree with delay and DR capacity constraint (BTDR), is presented in this paper. It enables the node with large DR capacity to connect to the multicast tree with high priority in the process of constructing the multicast tree, reducing the response time of the DR device with large DR capacity. Simulations are conducted to demonstrate the approach presented in this paper.

Hardware-in-the-loop test of learning-based controllers for grid-supportive building heating operation

While MPC is the state-of-the-art approach for building heating control with proven cost savings and improvement in energy flexibility, in practice, buildings are operated by simple rules-based controllers which are not able to accomplish an energy efficient and flexible operation. This paper explores the suitability of deep neural networks for approximating optimal economic MPC strategies for this task. In particular, we develop a convolutional neural network controller and test it in a closed-loop simulation against MPC and an improved predictive rule-based controller. The learned controller is easy to implement and fast to process on standard building control hardware. The feasibility, performance and robustness of the learned controller is validated in a realistic hardware-in-the-loop test setup for the demand-responsive operation of a heat pump combined with a storage tank.

Model-predictive control for testing energy flexible heat pump operation within a Hardware-in-the-Loop setting

Model-predictive control is a smart control technique that can unlock the potential of the building’s thermal production leading to improvements in energy efficiency or energy flexibility, which is obtained by a demand responsive operation based on external grid signals. This work explains the set-up of a Hardware-in-the-Loop experiment to test an energy flexible heat pump controller under realistic conditions. The developed simple, low-cost model-predictive control algorithm can easily be integrated with a traditional heat pump controller.

Correction to “Demand Response Operation of Electricity-Intensive Chemical Processes for Reduced Greenhouse Gas Emissions: Application to an Air Separation Unit”

Correction to “Demand Response Operation of Electricity-Intensive Chemical Processes for Reduced Greenhouse Gas Emissions: Application to an Air Separation Unit”

Packet‐based feedback control of electrical drive and its application to trajectory tracking of manipulator

SummaryIn systems disconnected from the external grid, such as mobile robots and vehicles, the effective use of renewables and energy harvesting techniques helps a longer operation with less weight and space for batteries. Power packet dispatching system is a promising measure to manage the complex power flow created by such distributed power sources of various profiles. In the system, power is transferred as a pulse, and information tag is attached to the pulse power in voltage waveforms. The physical integration of power and information realizes a smooth inclusion of sources and loads of different profiles and their decentralized operation. This paper discusses the application of the system to load control. We propose a decentralized packet‐based feedback control scheme. The successful operation of the proposed scheme is confirmed by an application to an electrical drive. In addition, the application to trajectory control of 2‐degree‐of‐freedom manipulator reveals the possibility of a peak‐power reduction based on a demand response operation of the proposed scheme. The results contribute to a realization of decentralized flow control of power packets based on the convenience of both the sources and the loads.

An improved decomposition based multi-objective evolutionary algorithm for the operation management of a renewable micro-grid

This study presents a multiobjective energy management to optimize the renewable microgrid operation while satisfying a demand response and various operation constraints. With regard to energy cost minimization, pollutant emission reduction for better utilization of the renewable energy resources, such as wind and solar, as a competitive objective is proposed. Moreover, for maximizing the renewable microgrid operators, demand response benefits satisfying the load demand constraints amongst other operation constraints are incorporated into the operations of the renewable microgrid. The overall problem is formulated as a mixed-integer nonlinear constraint multiobjective optimization problem with different equality and inequality constraints. In this paper, an improved decomposition based multiobjective evolutionary algorithm is presented for optimal operation of the renewable microgrid with renewable energy sources and various devices such as diesel generators, micro-turbines, fuel cells, and battery energy storage. To improve the optimization process, differential evolution (DE) and the niche guided mating selection strategy are incorporated. Meanwhile, decomposition-based multiobjective evolutionary algorithm-DE is extended to tackle the constrained optimization problem. Finally, the proposed algorithm is applied in a renewable microgrid, and its superior performance is compared with the conventional evolutionary algorithms such as the multiobjective genetic algorithm and the original decomposition based multiobjective evolutionary algorithm.

Demand Response Operation of Electricity-Intensive Chemical Processes for Reduced Greenhouse Gas Emissions: Application to an Air Separation Unit

A recent push for reduced greenhouse gas (GHG) emissions has led (in part) to the addition of renewable electricity generation sources to the power generation mix. Renewables such as wind and solar are desynchronized from grid demand, requiring the use of fossil fuels to bridge the gap. We propose a novel production scheduling formulation, emissions-minimizing production (EMP), which utilizes time-based information on the nature of the power generation mix to lower GHG emissions related to the transmission and generation of electricity for industrial users. We demonstrate the application of EMP on a single-column air separation unit. The scheduling problem is cast as a mixed integer linear program that can be solved in a practical amount of time. Extensive numerical studies are used to place EMP in the context of other production scheduling methods (such as demand response), and demonstrate its potential for significant reductions in GHG emissions.

Optimal performance of electric vehicles parking lot considering environmental issue

In this paper, a bi-objective optimization framework has been proposed for solar photovoltaic-based intelligent electric vehicles parking lot in the presence of demand response program to satisfy both environmental and economic issues of parking lot. Time-of-use model of demand response program is utilized to promote both environmental and economic performance of parking lot. In fact, by using this program, the consumer will be encouraged to shift its energy demand from peak periods to off-peak ones and this will result in enhancement of environmental and economic performance of the parking lot. In order to solve such a problem with multiple objective functions, weighted sum and fuzzy decision-making algorithms are implemented. A sample model of parking lot with various energy resources like the upstream network, renewable and non-renewable energy resources such as solar photovoltaic system, wind turbine, and local dispatchable generation systems have been used in addition to a hydrogen energy storage system ensuring a reliable power to the energy demand. The proposed model is investigated from economic and environmental aspects and the results reveal that by applying demand response program, total emission and operation cost of the parking lot have been reduced up to 2.67% and 4.28%, respectively.

Data centers as a source of dynamic flexibility in smart girds

Data centers have a significant potential to become a major source of flexibility in smart girds. They consume currently roughly 3% of all the electricity produced globally and are expected to only increase their consumption as the world becomes more connected and digitalized.As data centers are required to operate without any interruptions, they use power protection systems and energy storages. This paper investigates the technical and economic feasibility of dual-purposing these power protection systems, the uninterruptible power supplies, and their batteries in data centers to perform primary frequency regulation services. While the topic of data centers and demand response has been extensively covered in the current scientific literature, the focus has been on the demand response enabled by server workload shifting or hardware-enabled peak shaving. Based on an extensive literature review, there is a knowledge gap in the literature concerning primary frequency regulation and dynamic response enabled by modern power electronics systems in data centers. In this paper, this knowledge gap is bridged by suggesting a novel approach of taking advantage of the bidirectional operations capabilities of the uninterruptible power supply systems, thereby enabling them to provide dynamic power response from their battery systems.The feasibility of this approach is examined with the proposed method, which includes (1) an analysis of the required energy for primary regulation and the availability of this energy in a typical data center, (2) a simulation of activation events and their impact on the service life of the battery systems, (3) reaction speed and reliability considerations of the operations, and (4) an economic feasibility and balancing market analysis.The results show that as primary frequency regulation is an energy nonintensive service and data center battery systems are by design oversized for redundancy reasons, typical data centers have more than ample amounts of energy to participate in the primary regulation without jeopardizing their own processes. The results also show that by maintaining reasonable levels of usage, the battery systems can be operated within their specifications, and the demand response operations will not cause premature aging of the battery systems. The reaction speed of the power electronics is found to be very high and easily meet the current market requirements.While the achievable revenue from the primary regulation service is small compared for example with the electricity costs of the data centers, it is still significant as there is little to no impact on the daily business of the data centers.

Multi-Agent Simulation of a Demand-Responsive Transit System Operated by Autonomous Vehicles

Shared mobility solutions improve traffic efficiency in cities. This paper addresses the design of an urban demand-responsive transit system operated by a fleet of autonomous modules as a public transportation service. We introduce a discrete event-based multi-agent simulation to reflect system dynamics at an operational level. Vehicles, customers, and intelligent stops are modeled as software agents. Each entity has its own decision-making and planning component. Vehicles compete for a limited number of order requests, and decentralized stops manage the matching between a customer and a prospective vehicle. Scheduling and routing events take place highly dynamically. A simulation study evaluates a demand-responsive operation as a replacement for a fixed-route bus service. Results show that a total demand of 2.3 million rides can be fulfilled with a fleet of around 43,000 six-seater modules. In comparison with the current bus system, the total energy consumption and the system’s utilization are improved, while transport times and distances result in values below the bus system’s performance and therefore must be optimized in further research.

An MILP framework for optimizing demand response operation of air separation units

Peaks in renewable electricity generation and consumer demand are desynchronized in time, posing a challenge for grid operators. Industrial demand response (DR) has emerged as a candidate for mitigating this variability. In this paper, we demonstrate the application of DR to an air separation unit (ASU). We develop a novel optimal production scheduling framework that accounts for day-ahead electricity prices to modulate the grid load presented by the plant. We account for the dynamics of the plant using a novel dynamic modeling strategy, which allows us to formulate the corresponding optimization problem as a mixed integer linear program (MILP). Further, we present a new relaxation scheme that affords fast solutions of this MILP. Extensive simulation results show significant reductions in operating costs (that benefit the plant) and reductions in peak power demand (that benefit the grid).

Operation Optimization in a Smart Micro-Grid in the Presence of Distributed Generation and Demand Response

With the application of distributed generation and the development of smart grid technology, micro-grid, an economic and stable power grid, tends to play an important role in the demand side management. Because micro-grid technology and demand response have been widely applied, what Demand Response actions can realize the economic operation of micro-grid has become an important issue for utilities. In this proposed work, operation optimization modeling for micro-grid is done considering distributed generation, environmental factors and demand response. The main contribution of this model is to optimize the cost in the context of considering demand response and system operation. The presented optimization model can reduce the operation cost of micro-grid without bringing discomfort to the users, thus increasing the consumption of clean energy effectively. Then, to solve this operational optimization problem, genetic algorithm is used to implement objective function and DR scheduling strategy. In addition, to validate the proposed model, it is employed on a smart micro-grid from Tianjin. The obtained numerical results clearly indicate the impact of demand response on economic operation of micro-grid and development of distributed generation. Besides, a sensitivity analysis on the natural gas price is implemented according to the situation of China, and the result shows that the natural gas price has a great influence on the operation cost of the micro-grid and effect of demand response.

Transient simulation of carbon dioxide booster refrigeration system with mechanical subcooler in demand response operation

Transcritical carbon dioxide refrigeration systems are becoming more common as refrigerant regulations increasingly require the use of refrigerants with low global warming potential. The use of carbon dioxide in the booster cycle is one path toward near-zero global warming potential, but improvements to the cycle performance particularly at hot ambient conditions are needed to match the efficiency of incumbent technology. Also, as connectivity and grid flexibility become increasingly important considerations, the ability to use supermarket refrigeration equipment to provide load shedding and shifting is a topic of growing interest. The current article examines a transcritical booster system with mechanical subcooling, using transient models validated against laboratory test results. The model is used to examine the relative reduction in power for a shed of refrigerating capacity at either the medium-temperature or low-temperature evaporator level.

Identification of Value Proposition and Development of Innovative Business Models for Demand Response Products and Services Enabled by the DR-BOB Solution

The work presented is the result of an ongoing European H2020 project entitled DR-BOB Demand Response in Blocks of Buildings (DR-BOB) that seeks to integrate existing technologies to create a scalable solution for Demand Response (DR) in blocks of buildings. In most EU countries, DR programs are currently limited to the industrial sector and to direct asset control. The DR-BOB solution extends applicability to the building sector, providing predictive building management in blocks of buildings, enabling facilities managers to respond to implicit and explicit DR schemes, and enabling the aggregation of the DR potential of many blocks of buildings for use in demand response markets. The solution consists of three main components: the Local Energy Manager (LEM), which adds intelligence and provides the capacity for predictive building management in blocks of buildings, a Consumer Portal (CP) to enable building managers and building occupants to interact with the system and be engaged in demand response operations, and a Decentralized Energy Management System (DEMS®, Siemens plc, Nottingham, England, UK), which enables the aggregation of the DR potential of many blocks of buildings, thus allowing participation in incentive-based demand response with or without an aggregator. The paper reports the key results around Business Modelling development for demand response products and services enabled by the DR-BOB solution. The scope is threefold: (1) illustrate how the functionality of the demand response solution can provide value proposition to underpin its exploitation by four specific customer segments, namely aggregators and three types of Owners of Blocks of Buildings in different market conditions, (2) explore key aspects of the Business Model from the point of view of a demand response solution provider, in particular around most the suitable revenue stream and key partnership, and (3) assess the importance of key variables such as market maturity, user engagement, and type of blocks of buildings as drivers to market penetration and profitability. The work presented is framed by the expected evolution of DR services in different market contexts and the different relationships between the main stakeholders involved in the DR value chain in different EU countries. The analysis also relies on the results of interviews conducted at the fours pilot sites of the DR-BOB project with key representatives of the management, operations, and marketing. These are used to better understand customer needs and sharpen the value proposition.

Comparison of Light Rail Streetcar Against Shared Autonomous Vehicle Fleet for Brooklyn–Queens Connector in New York City

Policy makers predict that autonomous vehicles will have significant market penetration in the next decade or so. In several simulation studies shared autonomous vehicle fleets have been shown to be effective public transit alternatives. This study compared the effectiveness of a shared autonomous vehicle fleet with an upcoming transit project proposed in New York City, the Brooklyn–Queens Connector light rail project. The study developed an event-based simulation model to compare the performance of the shared autonomous vehicle system with the light rail system under the same demand patterns, route alignment, and operating speeds. The simulation experiments revealed that a shared autonomous vehicle fleet of 500 vehicles of 12-person capacity (consistent with the EZ10 vehicle) would be needed to match the 39-vehicle light rail system if operated as a fixed-route system. However, as a demand-responsive system, a fleet of only 150 vehicles would lead to the same total travel time (22 min) as the 39-vehicle fleet light rail system. Furthermore, a fleet of 450 12-person vehicles in a demand-responsive operation would have the same average wait times while reducing total travel times by 36%. The implications for system resiliency, idle vehicle allocation, and vehicle modularity are discussed.

Utility demand response operation considering day-of-use tariff and optimal operation of thermal energy storage system for an industrial building based on particle swarm optimization algorithm

Utility demand response (DR) programs and time-of-use (TOU) tariffs are designed to reduce customers loads as well as energy costs during peak periods in commercial and residential buildings. While TOU tariff is structured based on dividing a day into several periods with different corresponding electricity prices, the concept can be expanded for developing day-of-use (D-TOU) tariff, where different days in a week are treated differently, as experienced for industrial loads. Further, the advantages of such tariffs can be realized when demand side management technologies such as thermal energy storage (TES) in conjunction with heat pump systems are utilized. The goal of this study is to evaluate the effects of D-TOU tariff with four day types in a week on DR programs operation for an industrial customer with TES charged by an electric heat pump. The four day types considered in a week include Monday as start-up working day, Tuesday to Friday as regular working days, Saturday as half-day working day, and Sunday as weekend day. To achieve the goal, TOU and D-TOU tariffs are modeled and the TES system equipment capacities and operation with cooling and heating tanks are optimized based on particle swarm optimization for an industrial building load. The results for various pricing scenarios show that D-TOU tariff can be beneficial to the utility company and customer, as peak loads for electricity in day types 1, 2, 3 and 4 are reduced by 54, 52, 47 and 44% and customer costs for electricity cooling are lowered by 14, 13, 11 and 8%.