Real-time Dispatch Research Articles

Demand Response Aggregation With Operating Envelope Based on Data-Driven State Estimation and Sensitivity Function Signals

With the increasing penetration of renewable energy sources (RES), the value of the demand response (DR) draws wide attention. In order to realize the coordinated dispatch of widely spread resources, the aggregation of the controllable residential loads is managed by a single entity, namely the DR aggregator. Under the price-based DR programs, the DR aggregators actively respond to the market signals to reach maximum welfare. To avoid the quality of electricity services being jeopardized, the operational constraints of the network should be considered by the DR aggregators. However, DR aggregators are not expected to have access to the monitoring equipment and have limited knowledge of the network states. Hence, in this paper, we proposed a DR aggregation with the operating envelope framework based on the representative signals produced by the distributed network operator (DNO) in the context of big data era. The DNO provides representative signals, including real-time state estimation and sensitivity functions, to the DR aggregators based on the proposed Semi-supervised Coupled Generative Adversarial Imputation Network (SC-GAIN) and big data analysis. The DR aggregators can realize the secure and efficient real-time dispatch of the controllable loads based on the received signals. The proposed framework was verified on the IEEE 33-bus and 123-bus systems. The case studies show that the proposed SC-GAIN algorithm can better deal with the missing data, and the learned sensitivity functions can effectively avoid the overestimation of the true DR potential.

Online Coordination of LNG Tube Trailer Dispatch and Resilience Restoration of Integrated Power-Gas Distribution Systems

Severe winter storms can damage critical components of integrated power-gas distribution systems (IPGS), such as pulling down distribution lines and freezing gas pipelines/sources, causing electricity and gas supply disruptions. Liquefied natural gas (LNG) has a high energy density and can be transported by trailers, hence has great potential in multi-energy restoration. This paper studies the application of LNG trailers in the IPGS restoration. The process that the post-storm restoration coordinated with the trailer dispatch and the emergency operation is fully explored. The trailer dispatch is modeled as a finite-state machine; An efficient policy is proposed to dynamically update the real-time dispatch strategy without future knowledge of uncertainties. For the emergency operation of IPGS, storm-induced damages are considered in the modeling. Then, an online framework is proposed to satisfy the need for IPGS restoration with trailers. Compared with the offline method, the proposed framework does not require future information, so is adaptive to environmental uncertainty and easy to implement. Also, the framework solves a dedicated problem involving the observed information in a single period, hence enjoys a high computational efficiency. The simulation results demonstrate the potential of LNG trailers in resilience enhancement and validate the efficiency of the proposed method.

Tri-stage optimal scheduling for an islanded microgrid based on a quantum adaptive sparrow search algorithm

An islanded microgrid includes a combined heating and power system as well as electric vehicles. It is a key method for increasing energy efficiency, reducing pollution, and achieving carbon neutrality. Considering the uncertainties caused by renewable energy, load, and electric vehicles, this study presents a modified tri-stage scheduling method to realise the real-time dispatch of the islanded microgrid. The novel tri-stage scheduling method incorporates an information feedback mechanism into the traditional tri-stage scheduling method to address the problem of the sub-optimal solution obtained by the traditional method, which results from the significant errors between the day-ahead forecast data and intraday forecast data. In addition, users are guided by the tri-stage real-time price to charge/discharge electric vehicles orderly to fill in the valley and shave the peak of the electric load. In this study, a quantum adaptive sparrow search algorithm is proposed based on the characteristics of the tri-stage scheduling optimisation model and the limitation that the original algorithm easily falls into a local optimum. In the case study, the superiority of the novel tri-stage scheduling method, modified algorithm, and orderly charging/discharging of electric vehicles proposed in this paper are evaluated in different seasons. In the vehicle-to-grid mode, the influence of different numbers of electric vehicles on the real-time dispatch of the islanded microgrid is analyzed and discussed.

Real-time dispatching of operating buses during unplanned disruptions to urban rail transit system

Unplanned disruptions to urban rail systems can result in widespread confusion and severe delays, and running bus bridging service is a common solution. Currently, transit operators usually adopt experience-based response strategy in such a way that spare buses from bus terminals and parking lots are used to run bus bridging services. Such a response approach can be effective when there are spare buses available at nearby terminals/parking lots. However, in case spare buses are not available or enough, it could be beneficial to dispatch running buses from nearby operating bus lines to ensure efficient response while incurring minimal inconvenience to on-board passengers. While this approach could reduce metro users’ delays, it causes extra delays and dissatisfaction to affected bus riders. In this study, we investigate the optimal bus dispatching problem for responding to unplanned disruptions of urban rail transit system with two bus fleet sources: (1) spare buses at terminals and parking lots, and (2) running buses from nearby operating bus lines. To obtain the optimal dispatching scheme, we develop an integer programming model, which achieves a balance between the delays of metro users and that of bus riders. Considering of the high uncertainty of bus headways which could affect the performance of the above deterministic model, we further develop a robust optimization model to obtain more reliable bus dispatching schemes. Case studies based on the data sets in Shanghai are conducted. The results demonstrate that our approach can be carried out in real-time, and employing running buses from operating bus lines shows great potential in reducing affected users’ delays. In addition, the robust model offers a more reliable and competitive solution when the headway varies widely.

Two-stage stochastic robust optimization model of microgrid day-ahead dispatching considering controllable air conditioning load

The wide application of wind power generation and photovoltaic (PV) power generation has greatly increased the uncertainty of power grid. Under the background of smart grid, peak shaving can be carried out with the help of users' equipment. This paper studies the cooling efficiency of air conditioning from the heat transfer process between room and outdoor, and establishes the air conditioning load control model. Based on this, a stochastic robust day-ahead dispatching model for microgrid is proposed, taking cost minimization as objective function. The day-ahead dispatching is considered as the first stage which considering the arrangement of electricity purchasing and generating of thermal power units. The real-time dispatching is regarded as the second stage. Based on the results of the first stage, real-time dispatching of the various situations of the renewable energy output can be carried out. The decision variables are air conditioning control and ESS charge and discharge arrangement. Then the results are fed back to the first stage, and the cost is minimized by iteration. In this paper, the double-layer problem in the second stage is transformed into a single-layer problem by mathematical method, and the Column-and-Constraint Generation (CCG) algorithm is used to solve the two-stage stochastic robust optimization model. Finally, based on the data of a microgrid in a central city, the effectiveness of the method is verified, and the economic superiority of the day-ahead dispatching model at stochastic and robust level is discussed.

Holistic modelling and optimisation of thermal load forecasting, heat generation and plant dispatch for a district heating network

Efficient operation of district heating networks requires a precise forecasting of the thermal loads and an optimised dispatch strategy for the available generation and storage portfolio. This paper presents a holistic modelling and optimisation approach: first, detailed process modelling and optimisation of power plants and thermal storages; second, a numerical model for dispatch optimisation; and third, machine-learning-based load forecasting. The work is based on operating data from the district heating network of the city of Ulm in Germany. The paper presents the modelling, validation and simulation results of stationary and instationary process simulation for a biomass-fired combined heat and power plant. The analysis identifies a potential to integrate additional renewable power by “power-to-heat” technologies into different parts of the process. The economic benefit is quantified by mixed-integer linear programming optimisation applied to the district heating network. In order to allow for real-time dispatch optimisation, a machine-learning-based thermal load forecasting method was developed and evaluated, based on a 72-h forecast horizon. In addition, the economic impact of prediction uncertainties is analysed with the numerical dispatch optimisation tool.

Coordinating ride-sourcing and public transport services with a reinforcement learning approach

Combining ride-sourcing and public transit services (with ride-sourcing service to address the first/last-mile issues) can bring many benefits, such as saving passengers’ trip fares, improving drivers’ earnings, reducing gas emissions, and alleviating traffic congestion. However, it still remains a challenging issue to coordinate ride-sourcing and public transit services through real-time order dispatching. In this paper, we model the order dispatching in a multi-modal transportation system as a large-scale sequential decision-making problem. A centralized algorithm is then proposed to dispatch idle drivers to arriving passenger orders and determine whether to advise passengers to use a combined mode of ride-sourcing and public transit services (if yes, the algorithm also needs to recommend an appropriate transportation hub). In particular, our proposed algorithm contains a reinforcement learning approach that estimates the long-term expected rewards, and an Integer Linear Programming (ILP) that matches idle drivers and waiting passengers in real-time based on both immediate revenue and the estimated long-term rewards. By evaluation on the real-world on-demand data and metro system in Manhattan, the proposed method shows remarkable improvement on the system’s efficiency under different density of supply and demands.

Robust online operation of power systems with advanced adiabatic compressed air energy storage and renewable generation

With the rapid growth of renewable generation in power system, the share of dispatchable power reduces, raising the need for large-scale energy storage to compensate for volatile renewable output. Advanced adiabatic compressed air energy storage (AA-CAES), advantageous in large capacity and emission-free, is promising for grid-level use. This paper studies the operation of power systems with renewable generation and AA-CAES. The off-design characteristics of AA-CAES are featured by variable efficiencies and limited low-load capacity. A battery-like linear model of AA-CAES is suggested, in which the coefficients can be calibrated from experiments or simulations. A robust online operation scheme is proposed which only requires the currently observable information and makes no reference to renewable forecast. In the day-ahead stage, the unit on–off status as well as lower and upper bounds of unit output and storage levels in all periods are derived from a robust unit commitment model. In the real-time dispatch stage, an optimal power flow problem with pre-allocated operation bounds is solved. Feasibility is guaranteed by the setting of operation bounds obtained from the robust model. Case studies illustrate the precision of the simplified AA-CAES model and validate the performance of the operation method. The results indicate that a linear model is suitable when AA-CAES operates in near constant-pressure conditions, whose error is 0.4% for the charging part and 1.4% for the discharging part. A non-anticipative robust formulation is superior to conventional two-stage robust optimization in feasibility enforcement under uncertainty, the later encounters an infeasible probability up to 42% when the power system lacks limited regulation sources.

A Stochastic Optimization Framework for Realizing Combined Value Streams From Customer-Side Resources

Due to numerous supporting policies aimed at decarbonizing electricity infrastructures in different regions of the world, customer-side resources are becoming increasingly valuable. Consequently, load serving entities (LSEs) which typically have access to these customer-side resources can use them for multiple services simultaneously. In this paper, we discuss a stochastic optimization framework for using clusters of residential HVACs, electric water heaters (EWH) and behind-the-meter (BTM) batteries, spread around the LSE’s distribution network, for energy arbitrage, peak shaving and market based frequency regulation simultaneously. Our framework captures the effects of controlling the consumption of the customer-side resources on the voltages in the LSE’s distribution network. We also discuss two real-time dispatch algorithms capable of eliciting fast response from the resources to derive regulation signals from the market operator with minimal voltage violations. We evaluate the optimization models and dispatch algorithms using a HELICS-based co-simulation platform and real-world data from New York Independent System Operator (NYISO).

Multi-stage optimal energy management of multi-energy microgrid in deregulated electricity markets

A multi-energy microgrid (MEMG) consisting of different forms of distributed generation, e.g., combined heat and power (CHP) units and renewable distributed energy resources (RDERs), is considered as a key technology for accommodating RDERs and for the introduction of multiple forms of energy sources into the electricity market due to the multi-energy complementarity and flexible operation modes. However, the MEMG is subject to source and demand uncertainties which are the primary obstacles to its market participation. The source and demand uncertainties will pose serious challenges to the management of the MEMG and incur the penalty cost to participate in the real-time market. To minimize the operational cost, a multi-stage optimal energy management system (EMS) for participating in the deregulated electricity market considering the cost of market participation and the additional cost (e.g. the purchasing natural gas cost and the depreciation cost of energy storage system) is proposed in this paper. The proposed EMS consists of forecasting stage, day-ahead scheduling stage and real-time dispatch stage. The long short-term memory (LSTM) is adopted for day-ahead data forecasting during the forecasting stage. In the day-ahead scheduling stage, the cost for participating in the day-ahead market (DM) is minimized based on the forecasted data. In the real-time dispatch stage, the cost for participating in the intraday balancing market (IBM) and the additional cost are minimized based on the rolling model predictive control (MPC) method. The proposed method is verified and compared with benchmark solutions. The numerical results demonstrate that the proposed solution can outperform the benchmark solutions and reduce the peak-to-average ratio of the total net-load of multiple MEMGs.

Battery Energy Storage Participation in Automatic Generation Control of Island Systems, Coordinated with State of Charge Regulation

Efficient storage participation in the secondary frequency regulation of island systems is a prerequisite towards their complete decarbonization. However, energy reserve limitations of storage resources pose challenges to their integration in centralized automatic generation control (AGC). This paper presents a frequency control method, in which battery energy storage systems (BESSs) participate in automatic frequency restoration reserve (aFRR) provision, through their integration in the AGC of an island system. A local state of charge (SOC) controller ensures safe operation of the BESS in case of disturbances, without jeopardizing system security when available energy reserves are diminishing. The aFRR participation factors of regulating units are altered when the storage systems approach their SOC limits, re-allocating their reserves to other load-following units. Restoration of BESS energy reserves is achieved by integrating SOC regulation in the real-time economic dispatch of the system, formulated as a mixed-integer linear programming problem and solved every few minutes to determine the base points of the AGC units. A small autonomous power system, comprising conventional units, renewable energy sources and a BESS, is used as a study case to evaluate the performance of the proposed method, which is compared with alternative approaches to secondary regulation with BESS participation.

Dynamic-decision-based Real-time Dispatch for Reducing Constraint Violations

This paper proposes a dynamic-decision-based real-time dispatch method to coordinate the economic objective with multiple types of security dispatch objectives while reducing constraint violations in the process of adjusting the system operation point to the optimum. In each decision moment, the following tasks are executed in turn: ① locally linearizing the system model at the current operation point with the online model identification by using measurements; ② narrowing down the gaps between unsatisfied security requirements and their security thresholds in order of priority; ③ minimizing the generation cost; ④ minimizing the security indicators within their security thresholds. Compared with the existing real-time dispatch strategies, the proposed method can adjust the deviations caused by unpredictable power flow fluctuations, avoid dispatch bias caused by model parameter errors, and reduce the constraint violations in the dispatch decision process. The effectiveness of the proposed method is verified with the IEEE 39-bus system.

Using Interim Recommitment to Reduce the Operational-cost Impacts of Wind Uncertainty

Using wind-availability forecasts in day-ahead unit commitment can require expensive real-time operational adjustments. We examine the benefit of conducting interim recommitment between day-ahead unit commitment and real-time dispatch. Using a simple stylized example and a case study that is based on ISO New England, we compare system-operation costs with and without interim recommitment. We find an important tradeoff-later recommitment provides better wind-availability forecasts, but the system has less flexibility due to operating constraints. Of the time windows that we examine., hour-20 recommitment provides the greatest operational-cost reduction.

Coordinating multiple resources for enhancing distribution system resilience against extreme weather events considering multi-stage coupling

Under extreme weather events with a certain warning time, the prevention and restoration of distribution systems (DSs) involve multiple processes, including pre-event prevention, DS degradation, and post-event fault isolation and service restoration. Multiple resources are coupled and interact with each other in the above processes, which makes it a complicated task to develop resilience enhancement strategies for DSs. In this paper, a multi-resource DS resilience enhancement method is proposed to assist DS operators in making decisions against extreme weather events. On one hand, in the proposed method, the prevention and restoration of DSs are formulated as multiple stages based on the physical process of DSs when affected by extreme weather events. On the other hand, distributed generations (DGs), mobile emergency generators (MEGs), remote-controlled switches (RCSs), manual switches (MSs) and operation crew teams are fully coordinated to enhance the resilience of DSs against extreme weather events. And multiple coupling relationships are considered, including the topology coupling relationships among each stage, the spatiotemporal coupling relationships of mobile resources, and the switching coupling relationships between MSs and operation crew teams. The proposed method is mathematically formulated as a mixed-integer linear programming (MILP) model. Several cases are introduced to verify the effectiveness of the proposed method. The results demonstrate that the degradation of the DS can be effectively reduced by the coordination of network pre-reconfiguration and resources pre-positioning before events. The recovery process can be significantly speeded up by the coordination of the network reconfiguration and the real-time dispatching of MEGs and operation crew teams after events.

Research on the valley-filling pricing for EV charging considering renewable power generation

The real-time dispatch of electricity grids faces two new challenges: the volatility of renewable energy power generation and the impact caused by the large-scale charging demand of electric vehicles (EVs). Under the premise that China's renewable energy power generation is a prior connection to the grid, this article aims to guide the coordinated charging of EVs through the design of the time-of-use charging price mechanism and to help achieve valley-filling. The peak and valley hours were divided according to the load of baseload units that do not include renewable energy power generation. A nationwide discrete choice experiment has been conducted to investigate the willingness to pay of 2637 Chinese consumers for charging at different times and to analyse the influence of heterogeneous factors. Finally, the power economic dispatch model has been used to simulate the effect of the charging time-of-use pricing on the cost and load of the regional power system. Policy recommendations are provided based on consumers' willingness to pay for charging at public charging stations and home chargers at different times on weekdays and weekends. The simulation shows that under the EV charging time-of-use price mechanism with a 50% price increase during peak hours and a 50% price reduction during valley hours, the total power cost of Beijing-West Inner Mongolia power systems is reduced by 302.3 million CNY compared with the current scenario. The average peak-valley difference of thermal power output can also be reduced by 11.7% on weekdays.

Fuzzy Decision-Based Optimal Energy Dispatch for Integrated Energy Systems With Energy Storage

The integrated energy system is an important strategic direction in the world’s future energy field, which will become the main carrier form of the energy future of human society in the next 30–50 years, directly affecting or even determining the future energy strategy pattern of the world. There are many types of integrated energy system. In the study of optimal dispatching of energy storage, the integrated energy system is modeled according to the energy transmission characteristics of the integrated energy system, which mainly includes the combined cooling, heating and power system and the multi-type energy storage system containing electricity and heat storage. Then, a two-tier optimal scheduling model for an integrated energy system with multiple types of energy storage as the core is established, divided into the day-head scheduling layer and the real-time dispatch layer. At the day-head scheduling layer, an optimization model has been proposed with the minimum cost of the optimization goal and the power network, heating network, cooling network, energy storage operation constraints and carbon constraints as constraints. Then at the real-time dispatch layer, utilize the fuzzy controller to dispatch and control the electric storage system and the thermal storage system. Finally, the verification simulation experiment is carried out in an industrial park. Besides, the energy efficiency, economy and environmental performance before and after the integrated energy system connected to the multi-energy storage device are compared and analyzed, and different scheduling methods are used to compare and prove the advantages of the scheduling method.

Evolutionary Priority-Based Dynamic Programming for the Adaptive Integration of Intermittent Distributed Energy Resources in Low-Inertia Power Systems

The variability and uncertainty caused by the increased penetrations of renewable energy sources must be properly considered in day-ahead unit commitment, optimal power flow, and even real-time economic dispatch problems. Besides achieving minimum cost, modern generation schedules must satisfy a larger set of different complex constraints. These account for the generation constraints in the presence of renewable generation, network constraints affected by the distributed energy resources, bilateral contracts enclosing independent capacity provision, ancillary power auctions, net-metering and feed-in-tariff prosumers, and corrective security actions in sudden load variations or outage circumstances. In this work, a new method is presented to appropriately enhance the integration of distributed energy resources in low-inertia power grids. Based on optimal unit commitment schedules derived from priority-based dynamic programming, the potential of increasing the renewable capacity was examined, performing simulations for different scenarios. To ameliorate the expensive requirement of computational complexity, this approach aimed at eliminating the increased exploration-exploitation efforts. On the contrary, its promising solution relies on the evolutionary commitment of the next optimum configuration based on priority-list schemes to accommodate the intermittent generation progressively. This is achieved via the collection of mappings that transform many-valued clausal forms into satisfiability equivalent Boolean expressions.

An optimized multi-objective reactive power dispatch strategy based on improved genetic algorithm for wind power integrated systems

The large uncertainties in wind power generation will bring great challenges to the analysis of optimal reactive power dispatch (ORPD). This paper considers a multi-objective ORPD strategy solved by a heuristic search algorithm that combines the elitist non-dominated sorting genetic algorithm with inheritance (i-NSGA-II) and a roulette wheel selection to optimize the operation of wind power integrated systems. The proposed ORPD strategy employs day-ahead predicted wind energy and load demand data to optimally set of the following control variables: i) optimal tap positions of on-load tap changers (OLTCs), ii) reactive demand set point of reactive power compensators and iii) active and reactive power outputs of wind farms (WFs) with the objectives to minimize: a) voltage deviations, b) active power loss, c) wind turbine harmonic distortions and d) number of switching operations of OLTCs. Because of the uncertainties of wind energy and load demand, hourly modifications of the day-ahead optimal results are also formulated to determine the real-time optimal reactive power dispatch. The proposed new ORPD strategy has been rigorously tested using IEEE 33-bus test system, PG&E 69-bus test system and modified real GB network. Results obtained confirmed the efficacy and applicability of the proposed strategy in both distribution and transmission networks.

Real-time optimization of active distribution networks with distributed energy resources participating in frequency regulation

Dispatchable distributed energy resources (DERs) in distribution networks are envisioned to aid frequency regulation for transmission systems. In this paper, a real-time optimal dispatch framework for DERs in distribution networks is designed to offer frequency regulation services simultaneously. Different from the existing research that distribution networks track uniaxially the predetermined auxiliary-services commands of the transmission system, here we regard transmission system frequency regulation as a black box and learn the parameters of its proxy satisfaction function from the perspective of DER optimization. To solve such a special optimization problem with control performance feedback, first we employ Gaussian processes to learn the satisfaction function, and, especially, build pertinent upper confidence bounds to achieve the optimal provision of ancillary services. Next, the primal-dual gradient projection process is embedded into the Gaussian process upper confidence bound algorithm to pursue the optimal DER dispatch. Accordingly, the output powers of DERs can be controlled in real-time: in disaggregate mode, they meet the goal of the distribution network; in aggregate mode, they provide a more satisfactory tie-line power flow to the transmission system. Simulations for illustrative systems are provided to validate the approach.

Real-time power dispatch in a standalone hybrid multisource distributed energy system using an Arduino board

This paper presents the implementation of a real-time automated energy management control in a RE hybrid system, integrated with backup and validated in a laboratory setup. The experimental setup used a fuzzy intelligent controller for energy management on the software tool platform, the control board layout designed with aid of the Proteus Design Suite 8.1 software and the Arduino MEGA2560 hardware platform board, uploaded from Arduino integrated development Environment (IDE). The utilized hardware platform has the ability to monitor the real-time voltage dissipated by each component and is balanced by the controller via the voltage regulator, by adjusting it to an acceptable and readable voltage of 5 V by Arduino to the load. Arduino IDE has been programmed and uploaded to the hardware platform using C++ language. Furthermore, there are two different Arduino types, Arduino MEGA and Arduino UNO. Arduino MEGA2650 was selected in this study, as it has a more pin size compared to UNO and it may further accommodate a hybridized system with more components. The experimental results, therefore, was observed through experimental work that was based on the Arduino control preferences; the model capable of providing automatic supply of power to the load without human interferences, visualized in MATLAB plotting.