Demand Response Operation Research Articles

Effective incentive based demand response with voltage support capability via reinforcement learning based multi-agent framework

With the ongoing advancements in power grids, it is crucial to maintain a balance between energy generation and usage to ensure the stability of power systems. Any disparity between the supply and demand of energy can lead to increased expenses for utility companies and consumers, which might potentially result in a widespread failure of the grid. To tackle this problem, this study offers the implementation of the Voltage Capability Incentive-Based Demand Response (VC-IBDR) system, which utilizes a multi-agent framework. The main goal is to reduce power usage by providing financial incentives, while also ensuring that voltage standards are satisfied at each node. In this method, the aggregator agent coordinates demand response operations by interacting with household agents that are equipped with home energy management systems. Every household utilizes a disjunctively constrained knapsack problem optimization method to effectively organize the consumption of home appliances, considering customer preferences and dissatisfaction. During demand response events, the aggregator agent works together with household agents to optimize appliance schedules, ensuring that voltage levels at each node remain within acceptable levels. The simulation is performed to assess the efficacy of VC-IBDR on a feeder that provides service to 25 residential households. The findings indicate substantial decreases in power usage, with an average decline in demand of 4.20% at the feeder level and a 10.41% decrease in peak power consumption throughout the day. Furthermore, the voltage values at each node stay consistently steady within the range of 0.96 to 1.0 per unit throughout the day.

Orchestrating Smart Grid Demand Response Operations With URLLC and MuZero Learning

Orchestrating Smart Grid Demand Response Operations With URLLC and MuZero Learning

Integrating process and power grid models for optimal design and demand response operation of giga‐scale green hydrogen

AbstractElectrolysis‐based hydrogen production can play a significant role in industrial decarbonization, and its economic competitiveness can be promoted by designing demand response operating schemes. Nevertheless, the scale of industrial supply plants may be significantly large (on the order of gigawatts), meaning that electricity prices cannot be treated as an input for scheduling problems, that is, the “price taker” approach. This article presents a framework for the optimization of a large‐scale, electricity‐powered hydrogen production facility considering its integration with the power grid. Using a computational case study, we present an iterative scheme for integrating the process model with a model for power grid optimization and capacity expansion, taking the popular GenX model as an example.

A variable neighborhood search algorithm for a public bus line with a demand-responsive operation during peak hours

ABSTRACT In this study, we propose a variable neighborhood search (VNS) algorithm to optimize the performance of a single bus line during peak hours where the passenger flows in one direction are typically much larger than the flows in the opposite direction. The system we propose aims to increase the frequency of the service towards the city center during morning peak hours, by allowing some of the vehicles to perform short-cut trips away from the city center. Just before the morning peak hours and based on the expected demand, the VNS algorithm decides which buses should visit all the stops ahead or take a short-cut during its return trip. Experiments show that with the demand-responsive system, total passenger travel time improves about 10% on average for a real-size benchmark instance. The performance of the system is also analyzed under different fleet size and capacity, duration of peak hours, and demand scenarios.

E-Mobility and Batteries—A Business Case for Flexibility in the Arctic Region

This paper provides a method for determining the economic incentives and limitations for a battery used for peak clipping, with the goal of finding an optimal mix between the battery’s power density and energy density. A ratio called the R-factor has been introduced, which helps determine the energy demand to curb the peak. The paper’s results embrace different investment scenarios showing what battery capacity can be expected, dependent on interest rates, payback time and potential savings in power tariffs due to curtailment. In addition, the paper introduces the “wrench and cut” concept, which can help improve the investment case for batteries by combining battery operations with standard demand response operations. In particular, the effect of using a limited form of demand response-based load deactivation together with a battery has been analyzed. The investigation provided raises a point that battery degradation must be taken into account to prevent the reduction of battery life and possibly the needed payback period. The ultimate target of the presented research refers to vehicle-to-grid/vehicle-to-building developments in the Arctic region, where a vehicle is considered a mobile battery and where flexibility can be delivered in a cost-efficient way.

System and Market-Wide Impact Analysis of Coordinated Demand Response and Battery Storage Operation by a Load-Serving Entity

Because of electricity markets, environmental concerns, transmission constraints, and variable renewable energy sources (VRES), coordinated operation of demand response (DR) and battery energy storage systems (BESS) has become critical. In turn, the optimal coordinated operation of DR and BESS by an entity can affect overall electricity market outcomes and transmission network conditions. The coordinated operation is desirable for the profit-seeking entity, but it may adversely affect the cost and revenues of other market participants or cause system congestion. Though few coordinated operation models already exist, our aim in this research is to provide a novel multi-objective optimization-based methodology for the coordinated operation of DR and BESS to boost market profit. Moreover, another goal is to simultaneously study the combined effects of such coordinated models on transmission networks and electricity markets for the first time. This paper has proposed a new method for coordinated DR and BESS utilization by a load-serving entity (LSE) to increase its profit. Moreover, it has employed agent-based modeling of the electricity systems (AMES) for testing our coordinated DR and BESS method under day-ahead market and transmission system conditions. Simulation results of case studies indicate that the operating costs of all LSEs decreased, and there was as much as 98,260 $/day in cost savings for BESS deploying LSE1. Although revenues of cheaper generation companies (GenCos) decreased, those of expensive GenCos increased or showed mixed trends. For example, GenCo 3 exhibits an 8765 $/day decrease in revenue for 25% BESS capacity, whereas a 6328 $/day increase in revenue for 37.5% BESS capacity. The variance of LMPs, widely used as a risk index, greatly decreased for the LSE utilizing the coordinated methodology, somewhat decreased for other LSEs but increased for cheaper GenCos with no LSE at the local node. Since BESS deployment decisions of an LSE can have system-wide or market-wide consequences, simulation analysis before deployment can help reduce market distortions or system congestions.

Blockchain-based dynamic energy management mode for distributed energy system with high penetration of renewable energy

The emerging blockchain technology is one of the most feasible solutions to decentralized and autonomous energy management in distributed energy systems (DESs). However, with the increase of renewable energy penetration in the DES, blockchain nodes will generate massive calculation tasks and cause high delay in energy trading. In this paper, we propose a dynamic energy management mode, which is tailored for the DES with high penetration of renewable energy. Firstly, a novel consensus mechanism is established by the proof of energy contribution. Particularly, the energy contribution value characterizes the credible transaction, emission reduction, demand response and system operation contribution of energy prosumers. Secondly, the model inversion process of blockchain SM2 encryption algorithm is simplified by using the verification data of nodes with high energy contribution, so as to improve the computation ability of the DES. Finally, an actual energy blockchain project with 300 renewable energy prosumers is analyzed as an example. The case study shows that this work can reduce the network delay to less than 2000 ms, which is more than double the operation efficiency of the energy trading in Ethereum. Moreover, by calculating the network delay under different conditions, it is concluded that the number of committee nodes has a greater impact on operational efficiency than the number of transactions in the new block and the total number of nodes.

Demand Response with Electrical Heating in Detached Houses in Finland and Comparison with BESS for Increasing PV Self-Consumption

Distributed electric power production by small-scale customers is increasing continuously. Photovoltaic production is a popular method of producing self-energy for customers. Additionally, power systems require more flexibility when weather-dependent renewable energy production increases. Small-scale customers can increase the self-consumption of self-produced energy by using batteries or a demand response operation. However, batteries require high investment, and demand response operations induce a loss of comfort. Customers who heat their buildings using electric heaters are a good target for demand response operations because their heating can be controlled with limited changes in the indoor temperature. The demand response potential of a building can be defined by simply using customer load profiles and knowledge of the outdoor temperature. Any other information is not required in the proposed novel method. A tolerable variation in indoor temperature corresponds to considerably smaller battery capacity, though it is still a significant amount. With an optimally sized photovoltaic system, it is possible to use both methods simultaneously to increase self-consumption. Maximal benefits can be attained from both methods if the battery system is used as a primary control and the demand response is used as a secondary control. The defined novel method for determining the demand response potential of small-scale customers can also be used when estimating the flexibility of a large customer group. Small-scale customers together can provide significant flexible capacity when their electrical heating is centrally controlled.

Investigating Impacts of CVR and Demand Response Operations on a Bi-Level Market-Clearing With a Dynamic Nodal Pricing

This paper investigates the impacts of conservation voltage reduction (CVR) on electricity prices, the local market, and technical issues in distribution networks. An increase in electricity demand is one of the key challenges for developing sustainable societies. An increase in electric consumption puts immense pressure on electricity providers, which forces them to apply for load reduction programs during peak-demand time intervals. The CVR is one of the popular methods for load reduction, but how it would impact the pricing process and electricity market at the distribution level needs further investigation. The proposed methodology includes a power tracing and loss allocation-based pricing method. Since the distribution networks are going to be confronted by penetration of distributed energy resources (DER), prosumers, and microgrids, it is important to have a comprehensive methodology. This paper deploys a bi-level optimization algorithm to consider the financial benefits of all participating agents. In addition to CVR, the demand response (DR) programs are considered to shift and curtail flexible loads by the distribution system operator (DSO) and prosumers, respectively. The price sensitivity of prosumers toward change in the network’s voltage for better planning is calculated. The operation costs/profits of DSO/prosumers decrease/increase during CVR and DR programs by 4.63% / 3%, respectively.

Non-Intrusive Detection of Occupants’ On/Off Behaviours of Residential Air Conditioning

Understanding occupants’ behaviours (OBs) of heating and cooling use in dwellings is essential for effectively promoting occupants’ behavioural change for energy saving and achieving efficient demand response operation. Thus, intensive research has been conducted on data collection, statistical analysis, and modelling of OBs. However, the majority of smart metres currently deployed worldwide monitor only the total household consumption rather than appliance-level load. Therefore, estimating the turn-on/off state of specific home appliances from the measured household total electricity referred to as non-intrusive load monitoring (NILM), has gained research attention. However, the current NILM methods overlook the specific features of inverter-controlled heat pumps (IHPs) used for space heating/cooling; thus, they are unsuitable for detecting OBs. This study presents a rule-based method for identifying the occupants’ intended operation states of IHPs based on a statistical analysis of load data monitored at 423 dwellings. This method detects the state of IHPs by subtracting the power of sequential-operation appliances other than IHPs from the total household power. Three time-series characteristics, including the durations of power-on/off states and power differences between power-off/on states, were used for this purpose. The performance of the proposed method was validated, indicating an F-score of 0.834.

Innovative PPP models in urban mobility sector: A case study of three Indian cities: Amritsar, Kochi and Surat

Developing an economy requires evolving infrastructure needs and investments. Public Private Partnerships (PPPs) have been critical and have eased the government stress of investments in infrastructure by bringing in economic development with improved efficiencies and cost optimisations. The private sector participation in India's transport sector has been majorly focusing on capital intensive transport infrastructure developments like highways, airports or ports. It is still at an early learning curve of PPP in terms of less capital heavy projects or service delivery-oriented contracts. The lessons and learnings of decades of doing road PPPs is now being expanded to other sectors like water, solid waste, as well as urban transit. The paper discusses the innovative PPP models in the mobility sector for the case cities of Amritsar, Kochi, and Surat in India. The E-Auto shift from diesel autos in Amritsar, private bus aggregation of individual operators to company in Kochi, and the women owned operator CNG auto in Surat; and the innovations undertaken by government entities by assuming the role of a facilitator, regulatory and contract manager to support private sector in filling the service gaps is documented in the paper. The paper also documents the need for a more demand responsive operation planning, better risk allocation, and balanced contract conditions to make the sector lucrative for the private sector players.

Flexible operation strategy for formic acid synthesis providing frequency containment reserve in smart grids

The demand-side contribution to grid frequency regulation is becoming increasingly important due to the growing penetration of renewable energy in the power system. Among energy-intensive industrial loads, chemical plants have a high potential to offer grid balancing services due to their existing control infrastructure and storage capabilities. However, applying fast ramp rates and providing time-critical grid services is not straightforward due to strict constraints and the nonlinear dynamics of chemical systems. Therefore, adaptive operating approaches are required to increase the process’s flexibility and facilitate the fast demand response operation. This work proposes a flexible operating strategy for the cooperative operation of a Polymer Electrolyte Membrane (PEM) electrolyser and multi-stage compression systems in a chemical process to provide Frequency Containment Reserve (FCR). This strategy aims to realise the desired power regulation dynamics on the grid side while maintaining the reactor’s optimal operating conditions, i.e., temperature, pressure and flow rate ratio. A techno-economic analysis is performed to obtain optimal operating points. The techno-economic analysis shows that operating the process at a baseload of 73% while offering the remaining capacity as a power reserve can create additional revenue and improve the economic profit by around 10%. The proposed approach is validated by dynamic simulations of a Carbon Capture and Utilisation (CCU) process for formic acid production. The results show that the proposed strategy can enhance the process’s operational flexibility and enable FCR provision with a limited impact on reactor efficiency (<1%).

Integrated energy management of a smart community with electric vehicle charging using scenario based stochastic model predictive control

Occupant activities in buildings are connected by different transportation measures. For smart communities, it is possible to synergize the energy management of smart buildings with the vehicle operation/travel information available from transportation infrastructure, e.g. the intelligent transportation systems (ITS). Such information enables the prediction of upcoming building occupancy and upcoming charging load of electric vehicles (EVs). This paper presents a predictive energy management strategy for smart community, which features water-based district cooling for a cluster of buildings driven by a multi-chiller central plant, and each building hosts a number of EV charging stations. A scenario-based stochastic model predictive control (SCMPC) framework, in which the upcoming building occupancy and charging load, ambient temperature, humidity and solar irradiance are assumed to be stochastically predictable. The SCMPC targets demand response operation at community level, involving both time-of-use and demand charges, and the combined power consumption by the central plant and EV charging observing to the transformer limit as a coupled constraint. To evaluate the propagation of the above uncertain factors through the receding-horizon SCMPC process, moving-horizon probabilistic models are established for EV arrival information. By developing a functional mockup interface (FMI) based co-simulation platform developed with Modelica and Python, the proposed method is validated via simulation study, and the performance indices are evaluated.

Fairness algorithm for emergency demand response operation in distribution networks

As a response to increased power demand, electrical utilities have started to use Demand Response (DR) programs to improve the stability, efficiency and reliability of their networks. One type of DR, an Emergency Demand Response Program (EDRP), addresses stability challenges by sending curtailment orders to consumers associated with the program. There are different methods for calculating the amount of power that should be curtailed and how it should be divided among consumers. An important aspect of curtailment assignment is fairness, yet there is no agreed-upon manner of achieving fairness in curtailment allocation. This paper presents a new fairness-based EDRP for a residential area. The fairness is manifested in the algorithm by taking into account the number of times and the amount of energy a customer is asked to curtail for a predetermined period of time. The proposed algorithm is demonstrated and verified using simulations.

An optimized demand-response operation method of regional integrated energy system considering 5G base station energy storage

The scheduling technology of regional integrated energy system is one of the key technologies to realize carbon neutralization by utilizing wind-power. Aiming at the optimal scheduling problem of regional electrothermal integrated energy system considering wind-power utilization and load side energy consumption, this paper proposes an optimized demand-response operation method of regional integrated energy system considering 5G base station energy storage. The regional integrated energy system of load side demand response is constructed based on the comprehensive consideration of technical and economic factors such as wind-power utilization and economic costs and load side peak valley difference. Finally, a two-layer particle swarm optimization method is proposed to solve the model. The experimental results show that the proposed method can effectively achieve wind-power utilization, economic dispatch and reduce the peak valley difference through load side demand response, which can improve the economic efficiency, environmental protection and low-carbon operation of regional integrated energy system.

Forecasting voltage harmonic distortion in residential distribution networks using smart meter data

This paper introduces a methodology to forecast voltage total harmonic distortion (THD) at low voltage busbars of residential distribution feeders based on the data provided by a limited number of smart meters. The methodology provides relevant power quality indices to system operators using only the existing monitoring infrastructure required for demand response operation. Different algorithms for voltage THD forecasting are implemented, including artificial neural networks, and their performance is tested and compared. The necessary coverage of smart meters for the acceptable accuracy of the estimated THD is also established. The estimation algorithms are validated considering probabilistic demand load model developed based on typical harmonic injections of household devices obtained from measurements and using a typical European low voltage test-feeder with 471 residential consumers.

Demand response to improve the shared electric vehicle planning: Managerial insights, sustainable benefits

Massive adoption of shared electric mobility benefits people’s daily commute and environment but creates overload issues into the power grid, then further cause challenges to charging service operations and power management. Previous research always focuses on single optimization process on shared vehicle planning, rather than the combination of demand management into day-ahead planning operations. To this end, we attempt to propose a mixed integer programming model integrating demand response operations to further explore the impacts of demand response on shared electric vehicle planning operations. We first model a two-stages model integrating charging facility location in the first stage and vehicle relocation in the second stage. Moreover, both supply-side and demand-side uncertainties are considered and approximated into tractable form by applying sample average approximation and distributional robust set featuring the entropy knowledge and electric vehicle’s multi-level charging duration. The demand response policy is also proposed to reshape the original charging demand into an economical and reliable way to improve operational efficiency and mitigate the power overload issues caused by massive electric vehicle adoption. Further, we conduct a real-world case study in Amsterdam, the Netherlands, to explore the social-operational impacts of vehicle planning optimization model integrating the demand response, robust charging facility planning in three areas: (1) The demand response integration promote electric vehicle planning operations on cost-saving for about 3%. (2) Data richness of serviceability towards charging piles influence all decisions through the shared electric vehicle charging station planning. (3) A trade-off exists between technical progress on charging rate and charging technology stability.

Identification and Online Updating of Dynamic Models for Demand Response of an Industrial Air Separation Unit

Demand-response operation of air separation units requires frequent changes in production rate(s), and scheduling calculations must explicitly consider process dynamics to ensure feasibility of the solutions. To this end, scale-bridging models (SBMs) approximate the scheduling-relevant dynamics of a process and its controller in a low-order representation. In contrast to previous works that have employed nonlinear SBMs, this paper proposes linear SBMs, developed using time-series analysis, to facilitate online scheduling computations. Using a year-long industrial dataset, we find that compact linear SBMs are suitable approximations over typical scheduling horizons, but that their accuracies are unpredictable over time. We introduce a strategy for online updating of the SBMs, based on Kalman filtering schemes for online parameter estimation. The approach greatly improves the accuracy of SBM predictions and will enable the use of linear SBM-based demand-response scheduling in the future.

Demand Responsive Market Decision-Makings and Electricity Pricing Scheme Design in Low-Carbon Energy System Environment

The two-way interaction between smart grid and customers will continuously play an important role in enhancing the overall efficiency of the green and low-carbon electric power industry and properly accommodating intermittent renewable energy resources. Thus far, the existing electricity pricing mechanisms hardly match the technical properties of smart grid; neither can they facilitate increasing end users participating in the electricity market. In this paper, several relevant models and novel methods are proposed for pricing scheme design as well as to achieve optimal decision-makings for market participants, in which the mechanisms behind are compatible with demand response operation of end users in the smart grid. The electric vehicles and prosumers are jointly considered by complying with the technical constraints and intrinsic economic interests. Based on the demand response of controllable loads, the real-time pricing, rewarding pricing and insurance pricing methods are proposed for the retailers and their bidding decisions for the wholesale market are also presented to increase the penetration level of renewable energy. The proposed demand response oriented electricity pricing scheme can provide some useful operational references on the cooperative operation of controllable loads and renewable energy through the feasible retail and wholesale market pricing methods, and thereby enhancing the development of the low-carbon energy system.

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.