Power Demand Side Research Articles

Performance analysis of hybrid microhydro power systems

Microhydro power (MHP) system can be an additional renewable energy technology option at many locations around the world to supplement universal access to modern energy stipulated by the Sustainable Development Goals (SDG). Modern microhydro power systems usually employ unregulated turbines and an electronic load controller, a demand-side power management device. Existing analytical models for such systems are lacking details, especially supply-side control, for performance simulation at hourly or sub-hourly scales. As presented in this paper, this work has developed a stochastic model for downscaling of streamflow and an empirical model of MHP system. These models were integrated within the framework of the Hybrid2 tool to simulate the long-term performance of hybrid MHP systems. Three design configurations are presented for a case study in Nepal. The results show that, along with a small pond that can store water for an hour, a hybrid MHP system with half the size of the battery bank can supply the load to comply with the Tier 3 level of energy access stipulated by the Global Tracking Framework (Sustainable Energy for All). Future use of this model will enable designers and developers of MHP systems to enhance performance and cost-effectiveness of hybrid renewable energy systems.

A Control Approach Integrating Electric Vehicle Charger and Dynamic Response of Demand-Side Resource Aggregators

Regardless of whether level 1, level 2, or dc fast charging, several methods are under development or available as an aftermarket option for Plug-in electric vehicles (EVs). While confining the charging behavior of EVs to low-load periods is difficult, it leads to significant and uncertain load peaks and valleys in the utility power network. Therefore, this paper proposes a new method for an electric vehicle charger. The proposed charging method involves power supervisory characteristics on the basis of the paradigm of a constant-current and a constant-voltage (CC–CV) charger so that the external commands from the demand-side resource aggregator in the smart grid can be executed. In the method, first, a feedback loop is included in the controller to formulate the relationship between battery voltage and charging power as a quadratic concave downward polynomial. The controlled output power at the beginning of constant voltage charging is then set to the maximum value of the quadratic polynomial because the charging power requirement near this point is closed to the highest. Hence, if the charging power is lower than the allowable maximum power, the proposed charger is determined to have followed the traditional CC–CV charging steps. On contrast, the proposed circuit launches the power control loop when the actual charging power is beyond the maximum power limitation of aggregator. The proposed charger can keep the power consumption inside an allowable range of aggregator. That is the power consumption at the EV charging sites becomes directly programmable by their aggregators. The proposed method is examined by conducting an interactive test of a remote energy management system and the proposed controller. The results revealed that the proposed method provide an easy method to dynamically control consumption pattern of the EV charging for demand-side power management.

An Event-Driven Convolutional Neural Architecture for Non-Intrusive Load Monitoring of Residential Appliance

Nowadays, the advancement of non-intrusive load monitoring (NILM) is hastened by the everincreasing requirements for smart power utilization and demand side management. Thus, an intelligent event-driven non-intrusive load monitoring method based on convolutional neural network (CNN) is proposed in this article to profile residential consumer behavior, which focused on limited applicability and low recognition accuracy of existing NILM techniques. The deep convolutional neural architecture improves the performance of load monitoring procedure, and extends its application range to monitoring process in terms of various and combined characteristics. Meanwhile, the event-driven procedure is conducted to find the start-stop time and state change of the appliance accurately, including zero-cross detection, current similarity detection, threshold evaluation, and event current acquisition. Subsequently, the current-to-image conversion is carried out to represent the characteristics of residential appliance as the input of CNN. And the convolutional neural architecture is proposed to identify the power consumption load and its working condition. Selected experimental results are performed that the average recognition accuracy is over 93%, which demonstrates the effectiveness and superiority of the proposed methodology for home energy management.

Power Saving Management Based on Simulation of Domestic Air Conditioning Operation

With the continuous increase of building area and the improvement of people’s economic level, the penetration rate of air conditioners in China has continued to rise. Scientific residents’ air-conditioning behavior not only helps users save power, but also has important significance for rational planning on the power demand side, smooth operation of the power grid, and alleviating the contradiction between power supply and demand. This paper combines the thermal comfort theory of the human body with the thermal balance theory of the room, and establishes the thermal balance equation and thermodynamic model of the air-conditioned room. Different air-conditioning behavior models are designed for simulation. Through simulation, according to the simulation results, this paper puts forward air conditioning behavior recommendations for residents with different living habits in different cities in the north and south.

An Evaluation and Optimization Methodology for Efficient Power Plant Programs

Power demand side management (DSM) regulations stipulate that electric power grid enterprises of cities and provinces must complete their target of power saving through efficient power plant (EPP) programs. This investigation studies power DSM and establishes a supervision and evaluation system for the EPP program. The proposed system can be used to evaluate the performance of an EPP program such as electricity saving and investment benefit. A model of EPP is established. A cuckoo search algorithm is applied for the model optimization and its result gives an optimal decision scheme. Finally, a fuzzy comprehensive evaluation method is used to assess an EPP program. The results show that the established supervision and evaluation system and the model of an EPP program are effective and applicable for power grid enterprises.

The adaptive assessment method for different energy storage applications in large-scale re-electrical load access regions

In the context of energy transformation, re-electrification has become an important way to build a clean and low-carbon energy system. The large-scale re-electrical load access further increases the flexibility requirements on the user demand side. The application of the energy storage system (ESS) can not only improve the degree of electrification of the energy system but also improve the energy utilization efficiency. This paper analyzes the different development modes and key characteristics of energy storage on the power supply side, grid side and demand side in large-scale re-electrical load access areas. Five dimensions (such as storage security, technology maturity, system cost, storage scenario suitability and storage scalability) are selected to characterize the feasibility of a certain energy storage technology in a given application scenario. For different application scenarios, studying the appropriate development mode and its adaptability to the environment will help the healthy and sustainable development of energy storage.

Regional Power System Planning Optimization Considering Demand Side Resources

Power demand side resources include interruptible load and transferable load. With the aid of demand side resources, we can reduce peak load of power system and slow down the power grid investment, and promote the efficiency of power system operation. Based on the assessment of the potential value of demand side resources, this paper proposes a regional power system planning optimization model considering demand side response. The regional total costs of investment cost, fuel cost and demand response compensation cost are minimized with power system planning and operation constraints. The benefits of the proposed model are investigated through several case studies.

Optimal operation of regional integrated energy system based on demand-side load response

The integrated energy system (IES) has the advantage of improving energy utilization and promoting energy flexibility. From the perspective of demand-side load response, this paper establishes demand-side power, thermal load response, and natural gas demand response models, and then constructs the objective function of the lowest operating cost of the regional IES for combined electric heating and gas supply, using Cplex to perform optimization. Finally, a typical northern park is taken as an example to analyze and verify the feasibility of the model and algorithm. The analysis of the case shows that considering the electric heating gas demand side response will be better than not considering or considering only the single and both responses, not only can reduce operating costs, achieve peak reduction and valley filling, but also reduce abandonment of wind and energy, and increase energy utilization rate.

Demand-Side Power Paradigm-Oriented Analysis of Reactive Electric Spring Stabilization Capabilities

Electric Spring (ES) technique is a user-level solution developed to stabilize the supply voltage of a user under variations of the grid voltage. This paper analyzes the stabilization capabilities of a reactive ES that operates according to the demand-side power paradigm. By help of a convenient ES modeling, the extreme values of the active power that a user can draw under the ES action are first determined. Then, it is demonstrated that the demand-side power paradigm is fulfilled only if the distribution line impedance has a resistive component while its reactive component weakens such fulfillment. Lastly, the variations of the grid voltage that ES is able to cope with are worked out. All findings are formulated in terms of normalized quantities and consequently are of general validity. Computer-aided simulation of a case study exemplifies the theoretical findings.

Advanced Supervisory Control Plan with Power Saving Strategy for Demand Side Load

Demand Side Power Management is getting crucial to control the gradual rise of electrical power usage day-by-day. Though, the spread of power saving devices is a huge success throughout the world, yet the demand side power management system have to be more efficient and controlled. Here, an unique power saving strategy was established by intranet based Advanced Supervisory Control system. The core system consists of XPSU, XCU, PBU, DS and ASC. Planning of this system has been done not only to solve the above-mentioned criteria but also to reduce huge electricity demand from national grid. To reduce the National Grid pressure, full utilization of renewable energy grid output is ensured in this strategy. By this Control Plan, maximum 31.9% of the instantaneous power has been saved by 11.11% of System-end ASC Switching. A new idea, Electrical Mapping is also introduced for every different load section; by which the load behavior and power consumption history of our different load site area can be understood.

Electricity Market Reform Support Through Enhanced Power Transaction Model: In Relation to Optimization in Applied Mathematics

Given that the electricity trading market has been specified, some of the growing demands include the establishment of long-term mechanisms, the improvement of electric power emergency mechanisms, and the strengthening of electric power demand side. Therefore, this study proposed a model for optimizing the planning of the monthly power generation. Specifically, the grey model algorithm was examined to discern some of the improvements that could be implemented. With the energy conservation development direction and the market considered, the research setting involved a regional power grid dispatch management system. A key parameter that was examined involved the monthly free energy conservation strategy and how the rate of short-term energy saving establishment could be improved. From the findings, it was evident that the proposed model for optimizing energy-saving power generation establishments poses beneficial effects in terms of power generation scheduling and improved resource allocation, hence meeting the electric power market reform needs.

Special Section on New Trends in Residential Energy Management

The eleven papers in this special section focus on new trends in residential and home energy management. As an important branch of power demand side management, residential energy management plays an important role in reducing the emission and enhancing the energy efficiency in the energy delivery side. Recent technical advances bring significant transformations to energy end-users. First, increasing penetrations of residential renewable energy source, electric vehicle, and residential energy storage system have been transforming residential energy consumers to be “Energy Prosumers (Producer and Consumer. Second, the two-way communication infrastructure enables residential energy entities interact and exchange information flows with the external environment. Third, recent advances in ubiquitous sensing and metering technologies, such as Internet of Things, nonintrusive load monitoring, and advanced metering infrastructure, enable the deep understanding on behaviors of energy end-users and related environments. These technical advances consequently drive residential energy entities to become complex cyber-physical-social systems, which require newsolutions for coordinating, managing, and optimizing residential energy resources with the active participations of end users.

Resilience-oriented intentional islanding of reconfigurable distribution power systems

Participation of distributed energy resources in the load restoration procedure, known as intentional islanding, can significantly improve the distribution system reliability. Distribution system reconfiguration can effectively alter islanding procedure and thus provide an opportunity to supply more demanded energy and reduce distribution system losses. In addition, high-impact events such as hurricanes and earthquake may complicate the procedure of load restoration, due to disconnection of the distribution system from the upstream grid or concurrent component outages. This paper presents a two-level method for intentional islanding of a reconfigurable distribution system, considering high impact events. In the first level, optimal islands are selected according to the graph model of the distribution system. In the second level, an optimal power flow (OPF) problem is solved to meet the operation constraints of the islands by reactive power control and demand side management. The proposed problem in the first level is solved by a combination of depth first search and particle swarm optimization methods. The OPF problem in the second level is solved in DIgSILENT software. The proposed method is implemented in the IEEE 69-bus test system, and the results show the validity and effectiveness of the proposed algorithm.

Energy Revolution and the Energy Demand Side Management in China

China’s energy revolution has put forward new requirements for energy demand side management. For a long time, China’s energy demand side management lacks legalized and institutionalized policy and legal guarantee, which inhibits the role of energy demand side management in promoting sustainable economic and social development and energy structure transformation and upgrading. Based on the analysis of the status of China’s energy demand side management policies and laws and the existing problems such as low level of legislation, poor operability and focusing on power demand side management, suggestions on the legislation of China’s energy demand side management are put forward.

A Precision Analysis Method of Electrical Characteristics for Demand Response

In order to accurately grasp the users’ electricity consumption characteristics during the implementation process of the demand response (DR), a clustering algorithm was used to realize delicate analysis of demand-side users. Firstly, the typical load curve of a single user was extracted by means of averaging. Then, an improved adaptive bilayer spectral clustering algorithm was adopted to cluster typical load curves. The simulation results show that the method can accurately analyze the demand-side power load, the inner and outer spectrum clusters can meet the requirements of different demand response projects, can guide the development of the time-of-use price, and can calculate the interrupted response capacity. At the same time, it can provide a theoretical basis for consumption of renewable energy with DR.

The optimization of power transaction model with electricity market reform

With the specification of electricity trading market, to strengthen electric power demand side management, improve the mechanism of electric power emergency and establish a long-term mechanism, this paper proposed the monthly power generation planning optimization model based on the grey model algorithm. The paper studied the regional power grid dispatching management, according to the market and the development direction of energy conservation. This method paid attention to monthly free energy conservation strategy, meanwhile, improved the rate of short-term energy saving establishment. The optimized energy-saving power generation establishment can be more comprehensive for resource allocation and scheduling in the power generation scheduling, and to better meet the needs of the electric power market reform.

Monthly electricity purchase and decomposition optimization considering wind power accommodation and day-ahead schedule

With the development of wind power generation, energy storage and demand side management technology, the schedule of monthly electricity purchase is more uncertain and risky. To enhance the executability and economy of the monthly market and reserve enough space to accommodate wind power, a bi-level decision model, which integrates demand side responses, energy storage, wind power accommodation and day-ahead electricity purchase schedules, is proposed in this paper. In the first layer, the monthly purchase volume and its hourly decomposition schedule are optimized. In the second layer, factors of uncertain day-ahead prices and economic risk are examined, and the corresponding dispatching schedule of energy storage system and thermal power in the day-ahead markets are further optimized to promote the monthly market benefit. The proposed methodology is illustrated using a realistic case study and related affecting factors are analyzed in detail.

An Optimal Energy Management Method for the Multi-Energy System with Various Multi-Energy Applications

As the development of the multi-energy system (MES), various ME applications are deployed. ME applications not only bring advanced functionalities to the MES, but also show great potentials in promoting the operation performance of the MES, especially improving the accommodation of renewable energy sources (RES). However, the realization of these potentials largely relies on the energy management, which shall facilitate the effective function of each ME application and the coordinated collaboration of all the ME applications. Without a comprehensive energy management methodology, ME applications may mutually interfere, which not only hinder the RES utilization, but also may harm the MES operation performance. In this premise, this paper integrates the energy management model of the combined cooling, heat and power plants, power-to-hydrogen/gas-to-power plants, and demand side management model of the EV charging loads into the energy management model of the MES, and proposes an comprehensive optimal day-ahead energy management framework to simultaneously improve the profit, RES utilization rate, and energy saving performance of the MES. To address the proposed optimization model, Elitist Non-dominated Sorting Genetic algorithm II algorithm is employed to heuristically find the Pareto-optimal results. Finally, case studies prove the effectiveness of the proposed methodology.

Research on Scaled Flexible Load Scheduling Platform Architecture Based on Spark Technology

With the adjustment of China's energy structure and the development of smart grid technology, the ability to adjust the flexible load on the power demand side plays an important role in the future development of the power system. Therefore, this paper proposes a scaled flexible load scheduling platform architecture based on Spark technology. The architecture consists of five layers, which can not only support the business needs of huge real-time data, but also be compatible with existing computer resources. The validity and efficiency of the platform are verified by a case study.

Research on Optimal Control Strategy of Ice Storage Air Conditioning System

Ice storage air conditioning technology is a very valuable power demand side management technology. This paper establishes the goal of minimizing the operating costs of ice storage air conditioning system for the whole day. The cooling capacity of air-conditioning refrigeration and ice melting refrigeration in ice storage air conditioning systems and the total cooling capacity requirement of ice storage air conditioning systems are limited to the optimization of constraints in model. The paper applies trust region algorithm to solve the established optimization model, and uses MatLab to program. The simulation results of the example verify the correctness of the proposed model and the programming procedure.