Flexible Load Control Research Articles

Optimization scheduling of microgrid comprehensive demand response load considering user satisfaction

The original load control model of microgrid based on demand response lacks the factors of incentive demand response, the overall satisfaction of users is low, the degree of demand response is low, the Time Of Use (TOU) price of peak-valley filling capacity is weak, and the peak-valley difference of load curve is large. Regarding the limitations of the current microgrid demand response model, this study further optimizes the flexible load control strategy and proposes a two-objective optimization model based on price and incentive. Meanwhile, the model is solved using an improved chaotic particle group algorithm. Finally, the microgrid load data were selected for simulation analysis. The simulation results showed that the comprehensive demand response of flexible control model proposed increased the overall satisfaction of users by 9.51%, the overall operating cost of microgrid suppliers decreased by 12.975/ten thousand yuan, the peak valley difference decreased by 4.61%, and the user demand response increased by 27.24%. The model effectively improves the overall profit of the supply side of the microgrid, improves the user satisfaction, and maximizes the linkage benefits of the supply and demand of the micro grid. In addition, the model effectively reduces the phenomenon of distributed power supply in the microgrid, and realizes the supply and demand matching of the whole load in the microgrid.

Height-posture and load coupling control methodology of URAPM and its application in active suspension control of multi-axle vehicles

Unidirectional redundant actuated parallel mechanism (URAPM) is the physical prototype of active suspension system, truck crane outrigger system, etc. As the number of adjustment mechanisms (AMs) increases to more than three, the difficulty of height-posture control of the upper component (UC) increases considerably. The AMs load control is also tough but highly underappreciated, which in turn causes height-posture disturbance. In this work, a convenient height-posture and bearing load coupling control methodology and its application in the active suspension system are presented. Firstly, by characterizing the force-deformation coupling relationship of the URAPM, a basic geometry and load coupling control model is proposed. And for the desired bearing load control, different optimal load allocation algorithms are developed and evaluated. Secondly, coupling control models for the flexible load control under stationary height-posture, and the flexible height-posture control under balancing load are constructed respectively. Finally, bench tests based on a six-legged URAPM (equivalent to a three-axle active suspension vehicle) validate the proposed method can achieve high precision, high convergence, and most attractively, the height-posture and load coupling adjustment with few iterations, thus demonstrating its good application prospect in low-speed off-road maneuvering of multi-axle vehicles and other related fields.

Study on multi-type flexible load control method of active distribution network based on dynamic time-sharing electricity price

Accurate load control is the basis of safe operation and optimal dispatching in active distribution network, and the demand response system of flexible electricity price mechanism and incentive mechanism is the premise of load control. Aiming at the complexity increase of load control caused by the integration of conventional loads, electric vehicles, and micro-grid multi-type user loads in the existing active distribution network, this study comprehensively takes the power consumption willingness of multiple typesof users in active distribution network into account for the first time. This study dominated by the user willingness selects a typical active distribution network load-related data onto a certain area to conduct a calculation example analysis. Firstly, a multi-type user load demand response model of active distribution network is constructed. Then, in order to achieve flexible control of active distribution network loads, a dynamic time-of-use electricity price game model considering the target function and the willingness of multiple usersis proposed, in order to achieve the maximum of user satisfaction and interests of power supply enterprises. Furthermore, the example results are compared with the intrinsic electricity price.Finally, the simulation results show that the time-of-use price established by the multi-type user load game is effective inguiding users to adjust their power consumption habits, making the load curve shift, controlling the electricity load within the maximum power supply limit, and thus alleviating the pressure of power grid expansion.

Photovoltaic capacity dynamic tracking model predictive control strategy of air-conditioning systems with consideration of flexible loads

Building photovoltaic (PV) power generation is intermittent, volatile, random, and uncontrollable; thus, the use of solar grid-connected power generation can lead to a series of problems, such as grid voltage fluctuations and power imbalance. As a typical flexible load, the scheduling and regulation of the heating, ventilation, and air conditioning (HVAC) system load can help a grid-connected solar grid achieve balanced and flexible operation. This study proposes a PV capacity dynamic tracking model predictive control strategy for air-conditioning systems with flexible loads. This strategy aims to effectively address the issue of unstable grid-connected output of solar PV systems. In particular, a solar radiation prediction model based on a long short-term memory neural network optimized by the grey wolf optimization algorithm was established, which effectively improved the model prediction accuracy. The cost function of HVAC flexible load control considers the law of solar PV output and the human thermal comfort model. A genetic algorithm (GA) is used to obtain the optimal control parameters for dynamic regulation. The GA was employed to efficiently and effectively optimize the control of flexible HVAC loads, considering the variability of the solar PV output and ensuring human thermal comfort. A dynamic regulation model was developed for a typical office building with a grid-connected solar PV power generation system. TRNSYS was utilized for verification based on the actual measured data and relevant meteorological parameters of the case building. A new evaluation metric, volatility, was proposed to evaluate net load fluctuation. The simulation platform based on the physical building yield results indicated a strong resemblance between the energy consumption curve under the predicted control conditions using the flexible model and the PV generation curve. Additionally, the net load volatility was measured to be 2.63. Compared with the predicted control condition without the flexible model, the net load volatility of the grid was reduced by 47.08%, and the energy-saving rate reached 10.89% in the summer.

Flexible load control of new energy based on improved genetic algorithm

In the medium and low voltage distribution network, the load form of users is complex and changeable. There are a large number of single-phase and two-phase loads connected to the distribution network, resulting in a three-phase unbalanced operation of the distribution network. With the development of the new energy, the high proportion of distributed new energy will further aggravate the three-phase imbalance of the distribution network. Therefore, this paper proposes a coordinated optimization framework of droop parameters based on the multi-converter droop control, which takes the minimum loss of the distribution network as the optimization objective, and optimizes the reference point and the slope of the VSC droop hierarchically. A small-signal stability optimization dispatching method for the VSC droop slope in the DC distribution network is proposed. By adding small-signal stability constraints to the slope optimization model, the optimal slope command and slope stability region which can ensure the small-signal stable operation of the system are obtained. Experiments show that the optimization model of the VSC small-signal stability slope can make the droop control instruction significantly improve the small-signal stability of the system to adapt to the intra-day source load power fluctuations with a small economic cost. The slope stability region pre-optimization model can provide a reliable stability slope upper limit for the slope optimization problem based on ensuring the system operation economy. The research in this paper can make full use of the flexible control ability of power electronic equipment, and then suppress the three-phase imbalance, which is of great significance to improve the security and economy of the distribution system operation.

Congestion management in electricity distribution networks: Smart tariffs, local markets and direct control

Increasing peaks from high-power loads such as EVs and heat pumps lead to congestion of electric distribution grids. The inherent flexibility of these loads could be used to resolve congestion events. Possible options for this are smart network tariffs, market-based approaches, and direct control of flexible loads by the network operator. In most instances, these approaches are looked at in isolation, without considering potential connections and trade-offs between them. In this contribution, we aim to bridge this gap by presenting an overarching design framework for congestion management mechanisms. We classify proposals based on design choices and qualitatively discuss their benefits and risks based on an extensive literature analysis. As there is no one-size-fits-all solution, we map possible risks and discuss the pros and cons of different mechanisms for various problem types. We caution against using market-based mechanisms for local congestion, as they can be susceptible to undesired strategic behavior of market actors.

Multi-level optimal dispatch strategy and profit-sharing mechanism for unlocking energy flexibilities of non-residential building clusters in electricity markets of multiple flexibility services

Well-managed demand-side flexibility can effectively alleviate the stress of power systems caused by the increasing renewable energy penetration. The development of smart grid technologies provides win-win opportunities for system operators and demand-side users. Buildings have great flexibility potential through smart control of flexible loads, passive thermal mass storage and distributed active storage. However, a few fundamental problems on flexibility quantification, optimal coordination and profits sharing are faced when aggregating buildings to reach sufficient size for biding multiple flexibility services in electricity markets. Therefore, this paper proposes a multi-level optimal dispatch strategy and profit-sharing mechanism for non-residential building clusters to maximize flexibility profits and reduce the complexity of the optimization. At building level, a model-based quantification method is developed to evaluate flexibility potential of multiple resources. At cluster level, the coordinated bidding strategy of the aggregator is proposed, and flexibility profits are distributed based on the cooperative game theory at the post-market stage. Test results show that the proposed dispatch strategy can reduce up to 20.4% of daily electricity cost at cluster level and ensure profit increase of individual buildings with different flexibility characteristics. The active participation of building clusters in electricity markets could reduce the use of costly backup generators.

Frequency control strategy for coordinated energy storage system and flexible load in isolated power system

The isolated power system has a simple structure with small inertia and no support from the large-scale power system, so the frequency stability problem is more prominent. A frequency control method based on coordinated control of flexible loads (FL) and energy storage systems (ESS) is proposed in this paper. The ESS adopts the droop control considering the state of charge (SOC) to quickly respond to the system frequency deviation and provide fast frequency support. Based on the load power–voltage sensitivity, the FL participates in frequency control by adjusting the load voltage and thus the load demand. Based on the two frequency regulation resources, the active–reactive cooperative control strategy is proposed to adopt the fast response characteristics of ESS to cooperate with the synchronous unit to prioritize frequency regulation and enable the FL reactive control strategy when the system disturbance is larger than the primary frequency regulation capacity of the synchronous unit. The effectiveness of the proposed control strategy for isolated power systems is verified by building a modified IEEE33 node islanding system simulation in the real-time digital simulator (RTDS).

Deep reinforcement learning based bi-layer optimal scheduling for microgrid considering flexible load control

Deep reinforcement learning based bi-layer optimal scheduling for microgrid considering flexible load control

Consumer Psychology Based Optimal Portfolio Design for Demand Response Aggregators

Demand response (DR) has received much attention for its ability to balance the changing power supply and demand with flexibility. DR aggregators play an important role in aggregating flexible loads that are too small to participate in electricity markets. In this work, a DR operation framework is presented to enable local management of customers to participate in electricity market. A novel optimization model is proposed for the DR aggregator with multiple objectives. On one hand, it attempts to obtain the optimal design of different DR contracts as well as the portfolio management so that the DR aggregator can maximize its profit. On the other hand, the customers' welfare should be maximized to incentivize users to enroll in DR programs which ensure the effective and flexible load control. The consumer psychology is introduced to model the consumers' behavior during contract signing. Several simulation studies are performed to demonstrate the feasibility of the proposed model. The results illustrate that the proposed model can ensure the profit of the DR aggregator whereas the customers' welfare is considered.

Adaptive time-delay control of flexible loads in power systems facing accidental outages

The accidental outages of generating units are increasing in power systems, which can bring huge power shortage suddenly and lead to severe system oscillations. The secure operation of power systems sometimes cannot be guaranteed only by regulating traditional generating units, due to the rapid regulation requirement of making up for power shortage. To address this issue, this paper proposes using emergency demand response (DR) to provide contingency reserve capacities by adjusting the power consumption of flexible loads (FLs). Firstly, in order to analyze the dynamic regulation process of power systems in accidental outages, the power system model in faulty condition is reconstructed to obtain the regulation power from well-running generators. On this basis, FLs are modelled and integrated into the novel reconstructed power system model to be as an alternative method of making up for the fast regulation capacities. Considering that the inevitable communication time-delay probably leads to the slowdown of response speed and endangers the system security, an adaptive time-delay control (ATDC) scheme is proposed and integrated into the control process of aggregated FLs. In this manner, the regulation speed of FLs can be accelerated, the control precision of response capacities can be improved, and the power system frequency deviations caused by time-delay can be decreased. Finally, the proposed models and methods are verified by numerical studies. The results in the test system show that the frequency deviations can be decreased effectively from −0.3276 Hz to −0.1337 Hz in accidental outages by using the ATDC scheme of FLs.

Scheduling and Control of Flexible Building Loads for Grid Services based on a Virtual Battery Model

This paper presents a framework for modeling, scheduling, and controlling residential thermostatically controlled loads (TCLs) to provide multiple grid services, such as energy shifting, peak load reduction, and ancillary services. A modeling method is proposed to characterize aggregate flexibility from heterogeneous TCLs using a virtual battery model. Based on the flexibility model, a multi-period optimal scheduling formulation is developed to best utilize the flexibility from building loads and maximize total benefits from stacked value streams. An algorithm is proposed to control individual devices to follow the desired power consumption in real-time. The proposed methods are illustrated and validated through simulations.

Modeling and flexible load control of combined heat and power units

The electric load change of a power unit must be regulated rapidly to achieve power system stability, and it is increasingly becoming urgent especially that large-scale renewable energies are connected to power grids. Combined heat and power units hardly improve their power ramp rate because they commonly operate in heat-led mode. This paper presents a coordinated boiler–turbine heating model, describes the dynamics between the heat source mass flowrate and electric power output, and furthermore provides a strategy for heat and power control. Simulation results show that with the heat and power control strategy, the change of electric power can instantly respond, the power ramp rate can reach 2.9% of the rated power per minute, and the stable controls of the main steam pressure and heat supply can remain unaffected. The findings demonstrate the effectiveness of the proposed strategy for flexible heat and power load control.

Emission-Aware and Cost-Effective Distributed Demand Response System for Extensively Electrified Large Ports

A large part of the electric power demand of commercial ports is particularly flexible. Nowadays, the most significant flexible loads of a commercial port are the refrigerated containers, known as reefers, and the shore-side electric power supplies for ships. Moreover, ports usually have a great potential for local energy generation from renewable energy sources. The real-time control of the numerous port flexible loads is difficult to be achieved with conventional control systems. Multi-agent systems (MAS) have been proved to be very efficient in complex systems and, as it is shown in this paper, they can also be effective for large ports. In this paper, a real-time distributed demand response system using MAS with hierarchical structure is proposed for extensively electrified large ports. The proposed control system ensures real-time limitation of ship emissions, and the reduction of operation cost and fast power variations caused by local wind parks. To achieve the above, the electric power demands of in-port reefers and shore-to-ship power supplies are suitably controlled. A fuzzy-logic-based system is exploited to estimate reefers’ desired utilization factor and integrated to the developed MAS. The efficiency of the proposed method is evaluated by detailed simulations of a realistic case study of a large commercial port.

Modeling and control of flexible loads for frequency regulation services considering compensation of communication latency and detection error

Demand response has been widely utilized to provide frequency regulation service for the power systems by adjusting the power consumption of flexible loads. The frequency regulation service is time-sensitive and generally realized by direct load control, due to the quick response requirement (generally a few seconds). Most of the existing studies assume that the control on flexible loads can be implemented immediately without communication latency (CML), and the system frequency deviations can be detected without errors (FDE). However, in reality, the CML and FDE are ever-present during the control process and can influence the effectiveness of regulation significantly. To address this issue, this paper develops the aggregation models of ON-OFF flexible loads and continuously adjustable flexible loads, respectively. The centralized and distributed control methods considering the CML and FDE are developed, respectively. On this basis, a novel hybrid control method is proposed to compensate the CML and FDE, in which the modification method is developed for improving the estimation accuracy of the FDE. The results in the numerical studies show that the maximum system frequency deviation extends from −0.112 Hz to −0.120 Hz and −0.221 Hz due to the FDE and CML, respectively. After the modification by the proposed hybrid control method, the maximum frequency deviation is decreased to −0.110 Hz, which is almost equal to the ideal value when there is no FDE and CML. Therefore, this research can compensate the CML and FDE well, which is useful for guiding demand response projects in smart grid.

Development and Field Demonstration of a Gamified Residential Demand Management Platform Compatible with Smart Meters and Building Automation Systems

Demand management is becoming an indispensable part of grid operation with its potential to aid supply/demand balancing, reduce peaks, mitigate congestions and improve voltage profiles in the grid. Effective deployments require a huge number of reliable participators who are aware of the flexibilities of their devices and who continuously seek to achieve savings and earnings. In such applications, smart meters can ease consumption behavior visibility, while building automation systems can enable the remote and automated control of flexible loads. Moreover, gamification techniques can be used to motivate and direct customers, evaluate their performance, and improve their awareness and knowledge in the long term. This study focuses on the design and field demonstration of a flexible device-oriented, smart meter and building automation system (BAS) compatible with a gamified load management (LM) platform for residential customers. The system is designed, based on exploratory surveys and systematic gamification approaches, to motivate the customers to reduce their peak period consumption and overall energy consumption through competing or collaborating with others, and improving upon their past performance. This paper presents the design, development and implementation stages, together with the result analysis of an eight month field demonstration in four houses with different user types in Istanbul, Turkey.

A Double-Layered Fully Distributed Voltage Control Method for Active Distribution Networks

The increased penetration of intermittent distributed generation in distribution grids causes occasional voltage variations. The ICT infrastructure can provide the appropriate means for novel techniques to handle this kind of problems. The proposed algorithm utilizes a power flow model of the distribution grid and integrates the detection and the solution of the voltage constraint violation into a distributed double layered framework. The optimal solution of the related resource allocation problem involves the control of flexible microgenerators and loads, which can assist the grid by providing ancillary services for voltage control. The method is based on a gradient descent and a consensus algorithm for the distributed calculation of the Lagrangian multipliers. The proposed algorithm is tested in different scenarios in order to demonstrate its effectiveness.

A MILP-Based Restoration Technique for Multi-Microgrid Distribution Systems

The main focus of the work presented in this paper is on the outage management of interconnected microgrids during islanded operation after being disconnected from the utility main supply. The proposed two-stage load restoration technique is formulated as a Mixed Integer Linear Programming (MILP) optimization problem with the sole objective of optimally restoring the maximum number of disconnected loads. The proposed technique is applied to a distribution system comprising of several microgrids, i.e., Multi-Microgrid (MMG) distribution system. In this proposed technique, the power transactions between individual microgrids are managed through (1) determining the schedule of local energy resources, and (2) by obtaining the control signals pertaining to local flexible loads. Flexible load control signals are prioritized through two incorporated Demand Response (DR) programs, namely emergency load shedding and preemptive load shifting. To quantify the restoration technique performance, a new index—denoted as restoration technique success index (SI)—is proposed. The effectiveness of the proposed restoration technique is verified through different test case scenarios and the obtained results are discussed.

Distributed control of flexible demand using proportional allocation mechanism in a smart grid: Game theoretic interaction and price of anarchy

Abstract Demand side management leveraging flexibility of electric loads is a promising approach to reduce peak demand and to maintain supply–demand balance in future power grids with large scale renewable penetration. Distributed control is an important tool used for managing the demand side. In this paper, we develop a distributed method for coordinated control of flexible loads. In an intra-day setting, based on the supply schedule of thermal generators and predicted supply of renewable generators, a central control authority sets up price to maximize social welfare. This can be done using proportional allocation mechanism assuming that the consumers are price takers. If the consumers are price anticipators, the corresponding situation is modeled using non-cooperative game theory and existence of Nash equilibrium is established. Selfish behavior of agents in such a game theoretic set-up can reduce the efficiency of the overall system as compared to centralized control. In this paper, we are able to bound the efficiency loss for our game. The lower bound on the price of anarchy (PoA) for our game is found to be 0.75. For a non-cooperative game in a smart grid with flexible loads having different operating constraints and having utility functions that are non-separable with respect to time, this result is a new contribution to the demand response literature.

A Cyber Physical Model Based on a Hybrid System for Flexible Load Control in an Active Distribution Network

To strengthen the integration of the primary and secondary systems, a concept of Cyber Physical Systems (CPS) is introduced to construct a CPS in Power Systems (Power CPS). The most basic work of the Power CPS is to build an integration model which combines both a continuous process and a discrete process. The advanced form of smart grid, the Active Distribution Network (ADN) is a typical example of Power CPS. After designing the Power CPS model architecture and its application in ADN, a Hybrid System based model and control method of Power CPS is proposed in this paper. As an application example, ADN flexible load is modeled and controlled with ADN feeder power control by a control strategy which includes the normal condition and the underpowered condition. In this model and strategy, some factors like load power consumption and load functional demand are considered and optimized. In order to make up some of the deficiencies of centralized control, a distributed control method is presented to reduce model complexity and improve calculation speed. The effectiveness of all the models and methods are demonstrated in the case study.