Demand Response Period Research Articles

Effects of occupant thermostat preferences and override behavior on residential demand response in CityLearn

As space heating accounts for 54% of annual residential electricity consumption in Quebec, demand response programs specifically target load shifting through the automated control of thermostat setpoints during peak hours. On a district scale, varied thermostat preferences and setpoint override behaviors can have an impact on the success of the demand response program. This study examines two unique occupant types (Average, Tolerant) in terms of thermostat setpoint preferences as well as three different occupant Levels-of-Detail (LoDs) and analyzes their effects on the energy flexibility provided during demand response periods. For our baseline scenario, LoD 1, a static setpoint schedule is used and there is no control of the heat pump, while LoD 2 and LoD 3 incorporate thermostat setbacks during demand response events. LoD 2 assumes the occupant is comfortable within 2°C from the setpoint while LoD 3 allows the occupant to override the DR setbacks. We estimate the flexibility services provided by a ten-house residential community through the automated control of heat pumps during a three-month winter period, and we implement and simulate our study in CityLearn using reinforcement learning-based control for district-level energy management. When comparing LoD 3 to LoD 1, electricity cost was reduced by approximately 12% and net electricity consumption was reduced by approximately 17% during demand response periods. Likewise, we find that LoD 2 could overestimate savings in net electricity consumption, cost, and peak demand by 5% compared to LoD 3. The number of hours where the indoor temperature deviated more than ▪ from the setpoint occurred for less than 5% of the timesteps on average for the 10 buildings for LoD 3 while still achieving significant net electricity consumption reductions, thus highlighting that we can provide energy flexibility services to the grid while balancing occupant thermal comfort. Finally, the agents learned optimal decision-making that reduced the number of overrides across the training episodes for both Average and Tolerant occupants. We thus present a multi-agent framework as a means for addressing various occupant setpoint preferences and override behaviors for the control of heat pump systems at the neighborhood level.

Development of a dynamic demand response quantification and control framework for fan-coil air-conditioning systems based on prediction models

The air-conditioning demand response has gradually become an effective approach to reduce peak load of the national grid. However, with the increasing penetration of renewable energy generation sources, static performance evaluation of demand response that only fits one certain scenario cannot meet the demandsofthe times. To address this issue, a novel dynamic demand response quantification and control framework was built based on the secondary development of building energy simulation software and multilayer perceptron algorithm. In this framework, the demand response evaluation method can be used for different global temperature adjustment strategies under different external disturbances and internal loads, and the dynamic demand response control method could be further adopted for load reduction prediction and setpoint adjustment recommendation. The performance prediction model based on artificial neural network can predict the maximum load shedding during demand response period with an average absolute percentage error of 15 %, and the prediction error of load shedding intensity is ± 1 W/m2 for over 80 % of samples. Besides, the setpoint adjustment prediction model achieved an average absolute percentage error of 3 % while its prediction error is ± 0.25 °C for over 99 % of samples. The results prove that this research can provide reasonable suggestions for customers and load aggregators to achieve accurate control of setpoint adjustment in dynamic demand response conditions.

A comprehensive scheduling model for electric vehicles in office buildings considering the uncertainty of charging load

The access of electric vehicles (EVs) has a profound impact on the operation and scheduling of building energy system. However, due to the fixed schedules of EV charging load, few studies concerned on the coordination strategy between EVs and other potential flexible load in office buildings. In this study, uncertainty sources of EV charging load are extracted from sensitivity analysis based on regression method, and then the uncertainties are introduced into the charging load prediction model based on Monte Carlo method. A optimal scheduling model embedded with the uncertainty of charging load, with the joint operation of building air conditioning system, photovoltaic system and EVs, is proposed for building demand response period. The results show that the operation strategies obtained from the proposed model embrace the advantages of small load variance, high flexibility, low operating cost and large photovoltaic power consumption capacity. The arrival non-guarantee rate is reduced from 12.23% to 6.55% and the charging non-guarantee rate is reduced from 24.03% to 9.39%. On the basis of EV orderly charging and the air conditioning pre-cooling, 7.57–61.67% of the 8:00 startup load can be transferred to night, and the original load variance can be reduced by 3.06–17.81%.© 2017 Elsevier Inc. All rights reserved.

Improving the building energy flexibility using PCM-enhanced envelopes

Pre-formed internal insulative panels with impregnated phase change materials (PCM) can significantly increase both the thermal resistance and thermal capacitance of existing or new building envelopes, thereby improving the overall energy performance of buildings. A further advantage is that such measures have the potential to enhance the energy flexibility of the building, thereby offering the possibility of participation in demand side management measures such as demand response programmes. The current literature on building envelope physics lacks research on energy flexibility and demand response, especially in the context of the building envelope integrated design with high latent heat materials such as PCM for demand response applications. The objective of the current study is to examine how the addition of PCM impregnated building envelopes affects both the thermal performance of the building envelope, as well as the wider building energy characteristics when subject to different demand response events. A reference building is utilised, which is a residential detached house with a floor area of 160 m 2 and a south-easterly facing aspect. Another contribution of this study is proposing new energy flexibility indicators taking into consideration envelope pre-cooling and pre-heating periods prior to the demand response event. Simulation results show that shorter envelope pre-cooling periods (0.5 hr) together with longer demand response periods (4 h) are preferable for all envelopes to achieve the maximum power curtailment for cooling. PCM-enhanced envelopes are shown to give best cooling demand shifting and energy flexibility efficiency. The MW PCM-1 and MW PCM-2 envelopes have the highest flexibility efficiency with a value of 244%. For heating, gypsum board enhanced with PCM retrofitted on the envelopes are shown to give an overall good performance in energy flexibility efficiency and in power curtailment compared to the other building envelopes in all durations of an energy flexibility event. For heating, the maximum energy flexibility efficiencies range from 250% for the LW Gypsum Board envelope to 356% for the LW PCM-2 envelope. • Energy flexibility is mapped for building envelopes with sensible and latent TES. • Building envelopes are optimised for different demand response events. • An energy flexibility sensitivity analysis algorithm is developed in EnergyPlus. • In buildings with only sensible TES short demand response events should be used. • Envelopes with PCM have better energy flexibility in long demand response events.

Research on flexible smart home appliance load participating in demand side response based on power direct control technology

More and more flexible smart home appliance loads are participating in the demand-side response of the power system, which leads the complexity of power system load regulation is gradually increasing. In order to accurately regulate and control the flexible smart home appliance loads, this paper constructs a regulation architecture according to the load control method which is based on the load aggregator. And then, combined with the regulation architecture, this paper further establishes the value indicator of flexible smart home appliance loads. By analyzing the air-conditioner load proportion of smart homes on basis of the load value indicators, this paper effectively calculates the adjustable potential of the single fixed frequency air-conditioner and inverter air-conditioner load. Finally, through the simulation study, the demand response capability ΔPt and continuously controlled time Δtlast of air-conditioners are accurately calculated. Meanwhile, the adjustable potential of the air-conditioner load in smart home during the demand response period is obtained.

The short-term demand response potential and thermal characteristics of a ventilated floor heating system in a nearly zero energy building

Exploiting the thermal storage of building thermal mass is promising to increase the load shifting capacity. In this study, the energy consumption and thermal performance of a novel ventilated electric heating floor in a nearly zero-energy building in an extremely cold region in China were investigated. The numerical model of the heating system was first established and validated, then the energy consumption over one month were simulated through a rule-based control strategy in a traditional power system and a system with demand response, meanwhile, the impact of key structural and operational parameters was also analyzed. Results show that the energy consumption flexibility can be significantly increased through ventilation in both energy systems. Specifically, when ventilation is on throughout the period in the first energy system, the sum of energy consumption is reduced by 62.3% compared to that under non-ventilation mode. In the second energy system, the consumed energy can be reduced through ventilation in downward demand response period by 26.1%, and the energy storage is increased in upward demand response period by 4.6%. Besides, thermal stability can be increased through ventilation, and heat recovery time is reduced.

Reinforcement learning for whole-building HVAC control and demand response

This paper proposes a novel reinforcement learning (RL) architecture for the efficient scheduling and control of the heating, ventilation and air conditioning (HVAC) system in a commercial building while harnessing its demand response (DR) potentials. With advances in automated building management systems, this can be achieved seamlessly by a smart autonomous RL agent which takes the best action, for example, a change in HVAC temperature set point, necessary to change the electricity usage pattern of a building in response to demand response signals, and with minimal thermal comfort impact to customers. Previous research in this area has tackled only individual aspects of the problem using RL. Specifically, due to the challenges in implementing demand response with whole-building models, simpler analytical models which poorly capture reality have been used instead. And where whole-building models are applied, RL is used for HVAC control mainly to achieve energy efficiency goals while demand response is neglected. Thus, in this research, we implement a holistic framework by designing an efficient RL controller for a whole-building model which learns to optimise and control the HVAC system for improved energy efficiency and thermal comfort levels in addition to achieving demand response goals. Our simulation results show that by applying reinforcement learning for normal HVAC operation, a maximum weekly energy reduction of up to 22% can be achieved compared to a handcrafted baseline controller. Furthermore, by employing a DR-aware RL controller during demand response periods, average power reductions or increases of up to 50% can be achieved on a weekly basis compared to the default RL controller, while keeping occupant thermal comfort levels within acceptable bounds.

Contracted energy flexibility characteristics of communities: Analysis of a control strategy for demand response

Increasing energy system flexibility through demand-side measures will help meet challenges brought by the transition to a low-carbon energy system. Through participation in demand response programmes, buildings can act as sources of contracted flexibility. Contracted flexibility, in this work, is defined as energy flexibility that is supplied to fulfil a set of contractual terms that define when and how demand modifications are delivered and under which incentives or penalties. This paper identifies the factors affecting contracted energy flexibility potential of homes implemented with a model-predictive control strategy designed to deliver a simplified but yet generalisable incentive-based demand response scheme. The control strategy was implemented in centralised and naive-decentralised architectures using co-simulations to observe interaction of the controller with an English community of 30 homes fitted with air-source heat pumps. The results showed that the control strategy was able to deliver sustained demand reductions without violating comfort by preheating the homes prior to demand response periods, if conditions were suitable. Preheating the homes increased overall energy consumption and, in some cases, caused a peak in electricity demand prior to the DR period. Modifying factors of control operation, like the coordination strategy, magnitudes of penalties, control constraints and notice period between call for demand reduction and its delivery, were shown to affect the ability to deliver demand reductions. The contracted flexibility potential of the community was shown to be characterised by the buildings and their systems, the physical and contractual environment, and behaviour and preferences of the occupants.

Two Level Colocation Demand Response with Renewable Energy

Demand response is considered as a valuable functionality of the power grid and its potential impacts continue expanding with grid modernization. Colocation data centers (simply called colocation) are recognized as a notably promising resource for demand response due to their high power demand and remarkable potential in demand management. A major challenge of colocation demand response is the split incentive, that is, colocation operators desire demand response for financial compensation while tenants may not embrace demand response due to lack of incentives. Another key challenge is caused by renewable energy co-located with data centers. Demand response mechanisms overlooking the uncertainty of renewable would cause much inefficiency in terms of energy saving and economic aspects. Existing work considers the two challenges separately in the context of data centers. By contrast, this work jointly addresses them and specially studies mechanism design for colocation data centers in presence of co-located renewable. We propose a hierarchical demand response scheme, which is based on a new two-level market mechanism that results in a win-win situation for both parties, i.e., tenants who choose to reduce power demand obtain financial rewards from the operator, while the operator receives financial compensation from the electric power company due to its tenants' demand reduction. At each demand response period, the colocation operator solicits bids (amount of energy reduction) from tenants and tenants who choose to participate responds to the operator with their bids. The proposed mechanism provably converges to a unique equilibrium solution, and at the equilibrium, neither the operator or tenants can improve their individual economic performance by changing their own strategies. Further, we present a stochastic optimization based algorithm, which uses predictions of the co-located renewable to determine the colocation operator's best strategy. At the equilibrium, the algorithm has a provable economic performance guarantee in terms of the prediction error. We finally evaluate the designed mechanism via detailed simulations and the results show the efficacy and validate the theoretical analysis for the mechanism.

Flexibility-Based Energy and Demand Management in Data Centers: A Case Study for Cloud Computing

The power demand (kW) and energy consumption (kWh) of data centers were augmented drastically due to the increased communication and computation needs of IT services. Leveraging demand and energy management within data centers is a necessity. Thanks to the automated ICT infrastructure empowered by the IoT technology, such types of management are becoming more feasible than ever. In this paper, we look at management from two different perspectives: (1) minimization of the overall energy consumption and (2) reduction of peak power demand during demand-response periods. Both perspectives have a positive impact on total cost of ownership for data centers. We exhaustively reviewed the potential mechanisms in data centers that provided flexibilities together with flexible contracts such as green service level and supply-demand agreements. We extended state-of-the-art by introducing the methodological building blocks and foundations of management systems for the above mentioned two perspectives. We validated our results by conducting experiments on a lab-grade scale cloud computing data center at the premises of HPE in Milano. The obtained results support the theoretical model, by highlighting the excellent potential of flexible service level agreements in Green IT: 33% of overall energy savings and 50% of power demand reduction during demand-response periods in the case of data center federation.

A power limiting control strategy based on adaptive utility function for fast demand response of buildings in smart grids

Power imbalance in electrical grid operation has become a most critical issue that results in a series of problems to grids and end-users. The end-users at demand side can actually take full advantage of their power reduction potentials to alleviate the power imbalance of an electrical grid. Buildings, as the major energy end-users, could play an important role on power demand response in smart grids. This article presents a fast power demand limiting control strategy in response to the sudden pricing changes or urgent requests of grids within a very short time, i.e., minutes. The basic idea is to shut down some of active chillers during demand response events for immediate power demand reduction. The article focuses on the solutions to address the operation problems caused by the conventional control logics, particularly the disordered flow distribution in chilled water system. A water flow supervisor based on an adaptive utility function is developed for updating the chilled water flow set-point of every individual zone online. The objective is to maintain even indoor air temperature change among all zones during a demand response period. A case study is conducted in a simulation platform to test and validate the novel control strategy. Test results show that the proposed control strategy can achieve fast power reduction after receiving a demand response request. Simultaneously, the proposed control strategy can effectively solve the problem of disordered water distribution and achieve the similar changing profiles of the thermal comfort among different zones under the reduced cooling supply.

A First Look at Colocation Demand Response

Large data centers can participate in demand response programs\ and receive financial benefits by reducing energy consumption upon utility's request. However, the existing research has only considered demand response by owner-operated data centers (e.g., Google), leaving out another distinctly different yet integral part of the data center industry --- multi-tenant colocation data centers (a.k.a., colocation or "colo"), where the space is shared by multiple tenants for housing self-owned servers. A major hurdle hindering colocation demand response is "split incentive": the colocation operator may desire demand response, but lacks control over the tenants' servers; the tenants, on the other hand, can reduce server energy consumption but may not desire demand response unless they are properly incentivized. In this paper, we present a first-of-its-kind study on colocation demand response and propose an incentive mechanism, called iCODE (incentivizing COlocation tenants for DEmand response), which breaks the split-incentive barrier for colocation demand response. iCODE allows the tenants to voluntarily bid for energy reduction when demand response is needed and receive monetary rewards if their bids are accepted. We formally model tenants' bids and how the colocation operator decides the winning bids to maximize total energy reduction without profit loss. We demonstrate the potential of colocation demand response by using a trace-based simulation to show that iCODE can significantly reduce energy consumption (e.g., up to over 50%) during demand response periods.