Multiple Load Types Research Articles

Home energy management strategy to schedule multiple types of loads and energy storage device with consideration of user comfort: a deep reinforcement learning based approach

With the increase in the integration of renewable sources, the home energy management system (HEMS) has become a promising approach to improve grid energy efficiency and relieve network stress. In this context, this paper proposes an optimization dispatching strategy for HEMS to reduce total cost with full consideration of uncertainties, while ensuring the users’ comfort. Firstly, a HEMS dispatching model is constructed to reasonably schedule the start/stop time of the dispatchable appliances and energy storage system to minimize the total cost for home users. Besides, this dispatching strategy also controls the switching time of temperature-controlled load such as air conditioning to reduce the energy consumption while maintaining the indoor temperature in a comfortable level. Then, the optimal dispatching problem of HEMS is modeled as a Markov decision process (MDP) and solved by a deep reinforcement learning algorithm called deep deterministic policy gradient. The example results verify the effectiveness and superiority of the proposed method. The energy cost can be effectively reduced by 21.9% at least compared with other benchmarks and the indoor temperature can be well maintained.

Distributed prosumer trading in the electricity and carbon markets considering user utility

With the promotion of distributed energy trading and the transformation of power system operation mode, prosumers in the carbon and electricity markets face new opportunities and challenges. This paper proposes a peer-to-peer transaction platform to promote renewable energy consumption and carbon emission reduction, including multiple types of loads, coal-fired units, and microgrid prosumers. The carbon-emitting members of power generation and demand sides in the electricity market are introduced into the carbon market, and their carbon cost model is established. To guide consumption behavior of demand side, the user utility model based on electricity consumption is built to reduce carbon emissions. With the goal of optimizing the generation cost, user utility, and network fees, the distributed optimal scheduling model is established to recover the network fees. Finally, the alternating direction method of multipliers is used to solve this problem and protect transaction privacy. The results indicate that introducing carbon trading at the power generation and demand sides can promote the shift of members' production behavior from high-carbon emission industries to low-carbon ones. Additionally, an appropriate network fee mechanism and unit fee can guide market members to adjust their trading behavior adaptively and help the power grid recover costs.

Multi energy complementary optimization scheduling method for wind solar energy storage and charging integrated energy system

Abstract IES (The Integrated Energy System), consisting of distributed wind and solar power generation and multiple types of loads for cooling, heating, and electrical systems, is an important application scenario in the current energy configuration. It is not possible to balance multiple objectives like economy, carbon emissions, and wind and solar energy curtailment. Furthermore, there are numerous equipment that have multiple energy flows, complex conversion processes, and multiple scheduling requirements. Therefore, multi-objective optimization and minute-level scheduling strategies are key technologies to improve the utilization efficiency of comprehensive energy systems. This article proposes a comprehensive method for optimizing and scheduling energy systems that is based on multi-objective optimization and multi-time scale decomposition. Firstly, a comprehensive energy system architecture for wind solar storage and charging was constructed, and its operational characteristics were analyzed. Then, a multi-objective optimization scheduling model was established, which comprehensively considered multiple objectives such as system operating cost, minimum carbon emissions, and minimum wind and solar curtailment rate. Through time scale decomposition, the optimization scheduling problem was transformed into multiple subproblems and solved separately. Finally, it was verified through a case study. The simulation results show that the constructed model reduces the total operating cost by 5%, the wind abandonment rate by 7%, and the carbon emissions by 5.6% compared to the system without energy storage and charging piles. This verifies the effectiveness of the constructed model, reduces the system operating cost, and reduces the impact on the environment; compared with the DA-P (Day Ahead Programming), the proposed optimization method reduced the system’s electricity purchase cost by 7.51%, increased renewable energy utilization by 11%, and reduced carbon emissions by 6.1%. This verifies the feasibility of the proposed method in reducing system operating costs, improving system environmental protection, and stabilizing the system.

Laboratory Scaled-Down Cementitious Concrete Model Used for Estimating the Bearing Capacity of a Bridge Girder Based on the Similitude Theory.

Bridges are structures subjected to multiple types of loads and combinations during their service life. The uncertainties linked with the materials' behavior and manufacturing processes often necessitate the testing of produced elements on a real scale. This is particularly true for bridge concrete precast girders, which are frequently tested to predict the ultimate carrying load. Testing procedures are time-consuming, expensive in terms of both time and money, and involve a large amount of logistics and auxiliary equipment and devices. Thus, testing scaled-down models in laboratory conditions and extrapolating the obtained results with respect to the real-scale element using similitude theory has become a very common alternative method in the last decade. In this paper, experimental data regarding the efficiency of dimensional analysis computation are discussed. The proposed method involves comparing the values at which failure in bending and shear occurs for a 1:10 cementitious concrete bridge beam model with respect to the values computed for the prototype beam. Regarding the obtained results, a very small difference between the test results and the calculated values can be noticed.

Over-limit risk assessment method of integrated energy system considering source-load correlation

In an integrated energy system, source-load multiple uncertainties and correlations lead to an over-limit risk in operating state, including voltage, temperature, and pressure over-limit. Therefore, efficient probabilistic energy flow calculation methods and risk assessment theories applicable to integrated energy systems are crucial. This study proposed a probabilistic energy flow calculation method based on polynomial chaos expansion for an electric-heat-gas integrated energy system. The method accurately and efficiently calculated the over-limit probability of the system state variables, considering the coupling conditions of electricity, heat, and gas, as well as uncertainties and correlations in renewable energy unit outputs and multiple types of loads. To further evaluate and quantify the impact of uncertainty factors on the over-limit risk, a global sensitivity analysis method for the integrated energy system based on the analysis of covariance theory is proposed. This method considered the source-load correlation and aimed to identify the key uncertainty factors that influence stable operation. Simulation results demonstrated that the proposed method achieved accuracy to that of the Monte Carlo method while significantly reducing calculation time. It effectively quantified the over-limit risk under the presence of multiple source-load uncertainties.

Energy Conservation and Loss Reduction Strategy of Active Distribution System with Distributed Photovoltaic Resources Considering Conservation Voltage Reduction

With the massive distributed photovoltaic resources and multi-load connected to the distribution network, network loss and energy consumption have become two of the most important issues. To solve this problem, an energy conservation and loss reduction optimization strategy for active distribution network (ADN) considering conservation voltage reduction (CVR) is proposed. First, the probability density distribution of photovoltaic resources output prediction error is fitted by the Gaussian mixture model (GMM) to simulate the random characteristics of distributed photovoltaics. Second, according to the multiple load types, the load controlled by CVR is modeled, cooperating with the on-load tap changer (OLTC) to achieve energy reduction. Finally, the controllable resources are co-optimized to maintain the voltage level while saving energy. Case studies on the modified IEEE-33 node distribution network verify the applicability of the strategy.

Impact load identification and localization method on thin-walled cylinders using machine learning

In this paper, a novel impact load identification and localization method on actual engineering structures using machine learning is proposed. Three machine learning models, including a gradient boosting decision tree (GBDT) model based on ensemble learning, a convolutional neural network (CNN) model and a bidirectional long short-term memory (BLSTM) model based on deep learning, are trained to directly identify and locate impact loads according to dynamic response. The GBDT model and the CNN model can reversely identify force peak and location of impact loads. The BLSTM model can reconstruct the time history of impact loads. The method is verified on a thin-walled cylinder with obvious nonlinearity. The result shows that the method can accurately identify impact loads and its location. The characteristics of the three models are compared and the influence of structural boundary conditions on the accuracy of identification is discussed. The proposed method has the potential to be applied to various engineering structures and multiple load types.

Large-signal stability analysis of islanded DC microgrids with multiple types of loads

Due to the lack of support for the power system, the small inertia of the system, and the access to negative impedance elements, the stable operation of the islanded DC microgrid can be challenging. To ensure the stability of the system in a large operating range, a system-level large-signal stability analysis is carried out for a droop-controlled dc microgrid in this article. Firstly, the system-level equivalent model of the DC microgrid is established, the steady-state of the DC microgrid is analyzed and the instabilities principle, as well as their physical interpretation, is revealed. Second, necessary and sufficient conditions of the DC microgrid are deduced by the Lyapunov direct method, and the load limit of the DC microgrid is obtained under the restriction of stability and voltage deviation. Third, the correctness of the theoretical analysis is verified by the simulation results of a droop-controlled DC microgrid system.

Quantitative Analysis of System Operating Margin With the High Penetration of Distributed Generators in Distribution Networks

With the integration of distributed generators (DGs) and multiple types of loads, it is increasingly difficult to cope with the uncertainties of DGs, which brings challenges to the secure operation of distribution networks (DNs). Source and load cannot obtain the power balance locally, which may cause operational problems such as line power overload and node voltage violation. However, the methods for quantitatively analyzing the operating status of DNs have not yet formed a unified standard. For which reason, the concept of operating margin is proposed in this study, to represent the distance from the operating point to the boundary. In this study, to generally evaluate the operating status of DNs, the concept of node power margin is proposed first. Then the evaluation indexes including line power margin (LPM) and node voltage margin (NVM) are introduced. The quantitative calculation method for system operating margin (SOM) is further elaborated, which is supposed to be the evaluation indexes on system level. After that, the analysis process of evaluation methods for SOM is discussed. Finally, case studies are performed on the modified IEEE 33-node system to evaluate the operating margin of DNs and verify the enhancement brought by the energy storage system (ESS) and soft open point (SOP) through the flexible adjustment of power flow. The simulation results show that the distance between the DN and the security boundary is effectively quantified through the proposed method, the value can be easily calculated, and the change can be clearly reflected.

Load-Effect Separation of a Large-Span Prestressed Structure Based on an Enhanced EEMD-ICA Methodology

Prestressed structures have gained popularity in large-span buildings due to their great spanning capacity and light self-weights. This kind of structure is normally subjected to multiple types of loads, such as temperature load, wind load, and construction load. The determination of different load effects not only guides the design of similar structures but also helps reveal the damage-induced variation that would be concealed by the environmental loads. To determine the different load effects, separation of the load effects collected by structural health monitoring (SHM) is needed. This study presents an enhanced approach for load effect separation based on independent component analysis (ICA) and ensemble empirical mode decomposition (EEMD), called EEMD-ICA*. The proposed method is to minimize manual tuning of user-defined parameters, which makes the EEMD-ICA suitable for separating load effects from the different measured data. Specifically, an optimization method is developed to determine the appropriate level of the added white noise in the EEMD using the relative root-mean-square error (RMSE) index. A logarithm form of Bayesian information criterion (BIC) is employed for the robust estimation of the number of load effects in the ICA. Simulated structural responses from a square orthogonal cable-net are used to validate the effectiveness of the EEMD-ICA*. Then, the proposed methodology is employed to extract various load effects from the SHM data of the National Speed Skating Oval (NSSO), which is the largest single-layer cable-net structure in the world.

Large-Signal Stability Analysis of Islanded Dc Microgrids with Multiple Types of Loads

Large-Signal Stability Analysis of Islanded Dc Microgrids with Multiple Types of Loads

Modeling of integrated heat‐electricity systems considering integrated demand response and dynamic behaviors of district heating network

With increasing renewable energy integrated to the integrated energy systems (IESs), the dynamic characteristics and coupling effects of different forms of energy need to be taken into consideration. Dynamic behaviors of heating systems are not well captured by the traditional steady-state models of integrated heat-electricity systems (IHESs). As integrated demand response (IDR) technology is widely used in IHESs, it is of great significance to establish the models of IHESs considering demand side management and dynamic behaviors of district heating network (DHN). Firstly, in order to accurately reflect the real-time operation of the systems, a modeling method of IHESs containing source, network, and load is proposed. Then, based on the dynamic model of each equipment in the DHN and the demand response of multiple types of loads, a multi-energy flow dynamic model considering IDR and dynamic behaviors of DHN is proposed. In this paper, the models of the equipment are integrated under a unified framework, and a coordinated control model of the IHES is given, which aims to realize the economic operation of the system. Finally, the system model is placed in different scenarios for simulation. The results verify the dynamic characteristics of the DHN, and prove that the control strategy design for the IHES can help to improve the wind power consumption capacity and reduce costs.

A fast optimal load shedding method for power system reliability assessment based on shadow price theory

The optimal load shedding calculations of an enormous number of contingency states are the most challenging impediments for power system reliability assessment, especially in a large-scale system with multiple types of loads. To deal with that, this paper proposes a fast method that accelerates the calculation of optimal load shedding based on the shadow price. This method calculates the optimal load shedding of systems by shadow-price-based linear functions, rather than the time-consuming OPF algorithms. The hundred percentage criterion is used to look for the matching shadow price of states. Case studies are performed on the RTS-79 system. The results show that the proposed methodology can significantly simplify the solving process and reduce the calculation time.

Short-term load prediction of integrated energy system based on neural network

Considering the correlation and nonlinear characteristics of multiple types of loads in the integrated energy system, grey relation analysis (GRA) and long short term Memory (LSTM) neural network are selected to establish the short-term load prediction model of the integrated energy system. The model uses GRA method to analyze the coupling between multiple types of loads and the meteorological factors, and then obtains the load forecast results through the LSTM prediction model. Finally, a practical example is given to verify the validity of the model.

A Lagrange Multiplier Based State Enumeration Reliability Assessment for Power Systems With Multiple Types of Loads and Renewable Generations

With the integration of multiple types of loads and renewable generations, the number of system states significantly grows. As a result, running optimal power flow (OPF) to analyze a myriad of system states is challenging and this seriously restricts the efficiency of the state enumeration method. To address that, this paper proposes a Lagrange Multiplier based State Enumeration (LMSE) approach to accelerate the analysis without loss of accuracy. The core idea is to directly obtain the optimal load shedding of contingency states by Lagrange multiplier-based functions, rather than the time-consuming OPF algorithms. This approach can also be conveniently integrated with the impact-increment method and the clustering technique for further efficiency enhancement. Case studies are performed on the RTS-79 and IEEE 118-bus systems considering multiple types of loads, photovoltaics (PVs), and wind turbines (WTs). Results indicate that the proposed method can significantly reduce the computing time without compromising the calculation accuracy.

Real-Time Energy Disaggregation at Substations With Behind-the-Meter Solar Generation

Energy Disaggregation at substations (EDS) is challenging because measurements are mostly aggregated over multiple types of loads, and the existence of some loads such as behind-the-meter solar is unknown to the operator. This paper for the first time addresses this so-called “partial labels” issue in energy disaggregation and develops a model-free EDS method to separate individual loads, including BTM solar, from the total energy consumption in real-time. Our approach learns the patterns of all loads offline from recorded historical datasets with partial labels. Compared with conventional model-free methods that require either pure measurements of each load for training or full labels of each training sample, our method can extract load patterns from partially labeled aggregated data and thus, is more applicable to practical scenarios and alleviates the annotation burden for the operator. Specifically, we propose to solve a new dictionary learning problem, where column-sparsity and incoherence regularization terms are added to identify unlabeled loads and learn distinctive patterns of each load. In real-time disaggregation, our approach solves an improved sparse decomposition problem where one decomposes the aggregated measurements as a linear combination of some representative recorded measurements with known disaggregation learned in the offline stage. Numerical experiments are reported to validate our method.

Impedance modeling, dynamic analysis and damping enhancement for DC microgrid with multiple types of loads

The dc microgrid with multiple types of loads is considered to be a promising solution for integration of distributed generations (DGs). This paper presents the small signal impedance modeling of dc microgrid with multiple types of loads (including resistive loads, constant power loads and induction motor loads) which consists of source-side output impedance model and load-side input impedance model. Afterward, the Nyquist criteria based stability analysis is performed on the deduced small signal impedance models to investigate the interaction influence of different load states on the damping performance and stability boundaries of the dc microgrid system. Besides, a damping enhancement control strategy for dc microgrid is proposed in this paper based on the dual virtual inductive impedances. Since the dual inductive impedance can perform high-frequency attenuation feature during transients or oscillations, enhanced damping performance of the dc microgrid system is presented with the proposed control. Theoretical analysis and simulation cases show the validity and effectiveness of the proposed control strategy.

A novel adaptive balance-drive mechanism for industrial robots using a series elastic actuator

ABSTRACT To compensate the joint torque of Axis-II caused by end loading and mass of links in industrial robots, static balancing mechanisms are generally used. There are several existing balancing mechanism techniques. However, as these techniques lack autonomy, continuity and dynamics, they cannot completely compensate the joint torque in the presence of dynamic or multiple types of loads. In this paper, a new balancing mechanism technique for industrial robots, based on series elastic actuator (SEA) devices is presented. Simulation and experimental results prove that the devices combine the effects of the balancing mechanism and driving motor of Axis-II, as well as increase load capacity and flexibility. Therefore, this technique can be used to devise a novel dynamic balancing cylinder for heavily loaded industrial robots with active control ability, and simultaneously improve the load-to-weight ratio.

Reliability of solid rocket propellant grain under simultaneous action of multiple types of loads

A case-bonded solid propellant rocket grain is subjected to many stress-inducing loads during the service life, due to temperature, extended polymerization, transportation, vibration, acceleration, aerodynamic heating etc. and finally due to the operating pressure in the rocket motor. Composite propellant is a viscoelastic material whose mechanical properties highly depend on temperature and strain rate and sometimes may vary in the range of use of rocket motors for several orders of magnitude. Relationships between stresses and strains are much more complex than for the elastic material. Therefore, the stress and strain analysis and estimation of safety factor under the action of each individual load is quite complex and sometimes impossible. An even greater problem occurs when multiple different types of loads act simultaneously. An extreme case occurs in the moment of rocket motor ignition. Then, the very fast load acts due to the pressure, at which the propellant tensile strength is high. At the same time, the very slow thermal load acts on the grain, and in these conditions the propellant tensile strength is low. The vector addition of the stresses and strains due to different loads is not possible. It is also not possible to define the equivalent or resultant values of tensile strength and allowable strain. The principle of adding the current damage is applied here, similar to the model of cumulative damage. In addition, due to the large variations in mechanical properties of the rocket propellant, it is necessary to apply the methods of mathematical statistics for assessing the propellant grain reliability and service life.

State Estimation and Power Flow Analysis of Power Systems

State estimation and power flow analysis are important tools for analysis, operation and planning of a power system. In this paper, a new state estimation method based on the extended weighted least squares (WLS) method for considering both measurement errors and model inaccuracy is presented. Two bus, three bus, and IEEE 14 bus test cases are employed to evaluate the accuracy of the method. The comparison results show that the extended WLS method may outperform traditional WLS approach when the model is not accurate. In addition, this paper investigates a method based on Z matrix to implement power flow in a transmission system with multiple types of loads (e.g. constant PQ, constant impedance and constant current magnitude loads or mixed loads). The load flow results demonstrate that the method is effective and easy to implement when composite load types exist in the system. Our studies also show that it may be possible that multiple solutions exist for a power flow problem.