Flexible Power Consumption Research Articles

A comparative assessment of the power generation via S-shape and M-shape PV system and its impact on a residential consumer

This paper compares the differences in power generation and resident experience between S-shape and M-shape photovoltaic (PV) systems at the same location with the same number of modules. First, the sky anisotropy model (Hay-Davies-Klucher-Reindl, HDKR) is used to simulate solar irradiance (direct, diffuse, and reflection radiation) received by solar modules based on the laws of movement between the Earth and the Sun in the solar system. Next, the annual output power of two different PV system arrangements is calculated based on the solar irradiation received. Furthermore, because the solar module receives three types of solar radiation, interference is added to each of the three parts one by one to simulate shadows cast by moving objects such as birds, leaves, or clouds. Subsequently, off-grid and grid-connected power consumption models are established, and two different photovoltaic power generation systems are used at the power generation end. Lastly, the photovoltaic systems were tested based on residents’ electricity consumption habits, considering both off-grid and grid-connected households. Among them, the grid-connected model adopts the Particle Swarm Optimization (PSO) algorithm to generate an intelligent and flexible power consumption strategy. The results, based on data from Naha, Japan, show that with the same number of modules, the M-shape arrangement generates less annual power than the S-shape. In summer, though, the M-shape can generate more power, and the two structures have similar immunity to disturbances. The S-shape and M-shape have little difference in the power consumption experience. However, the general applicability of these results to other regions remains uncertain, and further testing is needed to account for environmental variations.

Analysis of Power Load Characteristics Based on Adaptive Ensemble Clustering Algorithm

With the growing number of power users and more flexible power consumption, how to cluster the power load curves efficiently becomes particularly important. In order to improve the accuracy, effectiveness and rapidity of load clustering, an adaptive integrated fuzzy clustering algorithm was proposed in this paper. Firstly, the initial clustering centroids were obtained by the improved DPC algorithm; then, under the dual guarantee of Euclidean distance and membership, FCM algorithm was used to cluster load curves; finally, the optimal number of clusters was obtained by the improved adaptive method in the iterative process; thus, the autonomous and accurate clustering of power load curve was realized. The proposed algorithm, DPC algorithm and FCM algorithm were applied to the load clustering experiment of power system respectively. The comparative analysis shows that this algorithm is effective and superior for load curve clustering.

Research on Modeling the Optimization of Power Load Combination for Power Sales Company

The power sales company is an important component of the power trading market. It can be used not only as the primary body for purchasing power from market transactions but also as the primary body for transferring power to the market and consumers, altering the distribution network’s power flow distribution and having a significant impact on the distribution network’s power supply capacity. The fundamental goal of the power sales company is to maximize the benefits by adjusting the power load distribution in response to changes in the power consumption of users. Based on the consideration of users’ flexible power consumption, this paper studied the load combination optimization modeling method of the power sales company. The building users are taken as the research object and the flexible power consumption type is determined based on various power values. The demand response data is extracted by load characteristic index and power consumption index, and the flexible power consumption law is predicted by a standardized matrix. Furthermore, using Hansen threshold theory, the load rate is computed and the grading is changed, and three groups of load combination schemes are obtained from the simulation test. Results show that the load combination of user 03, user 05, and user 07 is optimized in scheme C and the power sales profit of the power sales company is 26.31% and 22% higher than those of scheme A and scheme B, respectively. The proposed method can reduce the power purchase cost and increase the revenue of the power company by increasing the revenue from power sales.

An Approach to Calculate Electricity Costs for the German Industry for a System Efficient Design by Combining Energy Efficiency and Demand Response

This study analyses the German electricity price mechanism and laws used for the calculation of electricity costs. The goal is to find which price components and regulatory to relief energy-intensive companies are affected by energy efficiency and/or by demand response measures in a German company. Therefore the price mechanism and laws were used in a newly self-developed open source tool to easily and quickly calculate electricity costs. Grid charges increase significantly if the power consumption is increased. Even short peaks in the load profile may prevent individual grid charges for a company, i.e. an increase in electricity costs for companies. This price mechanism makes a flexible power consumption economically unappealing.

Strengthening the Group: Aggregated Frequency Reserve Bidding With ADMM

In a power grid, the electricity supply and demand must be balanced at all times to maintain the system's frequency. In practice, the grid operator achieves this balance by procuring frequency reserves in an ahead-of-time market setting. During runtime, these reserves are then dispatched whenever there is an imbalance in the grid. Recently, there has been an increasing interest in engaging electricity consumers, such as plug-in electric vehicles or buildings, to offer such frequency reserves by exploiting their flexibility in power consumption. In this paper, we focus on an aggregation of buildings that places a joint bid on a reserve market. The resulting shared decision is modeled as a large-scale optimization problem. Our main contribution is to show that the aggregation can make its decision in a computationally efficient and conceptually meaningful way, using the alternating direction method of multipliers. The proposed approach exhibits several attractive features that include: (i) the computational burden is distributed between the buildings; (ii) the setup naturally provides privacy and flexibility; (iii) the iterative algorithm can be stopped at any time, providing a feasible (though suboptimal) solution; and (iv) the algorithm provides the foundation for a reward distribution scheme that strengthens the group.

Piloting Demand Response in Retailing: Lessons Learned in Real-Life Context

This article presents a case study on a demand response (DR) pilot project dealing with the application of DR in a grocery store with the utilization of refrigeration equipment as energy storage and photovoltaics (PV) as an energy source. DR has recently gained increased interest due to the growing penetration of intermittent renewable energy requiring flexibility in power consumption. The smart power grid enables the introduction of novel solutions to increase flexibility and the entrance of new actors into the markets. Developing new solutions for the mainstream markets requires experimentation in real-life settings serving the development of technological capabilities, necessary policies and regulation, and user and market needs, as well as adaptation of and to infrastructure and maintenance systems. Our case study on a DR pilot in a grocery store in Northern Finland focuses on how the project contributes to knowledge on the potential for DR and scaling up. It was found that energy efficiency, DR, and self-generated PV power can be aligned and even enhance the potential for DR. While mature technologies exist, applications and installations have not yet been standardized to enable rapid scaling up, and current DR market rules and practices fail to accommodate for small electricity consumers.

Colocation Data Center Demand Response Using Nash Bargaining Theory

The huge yet flexible power consumption of data centers makes them promising resources for demand response, particularly for emergency demand response (EDR) which requires a certain amount of load curtailment during emergencies. However, current data centers often participate in EDR by starting up their backup diesel generators, resulting in both high costs and large carbon emissions. In this paper, we focus on cost-effective and eco-friendly demand response in colocation data centers by designing economic incentives for tenants to reduce their loads during emergency periods for EDR. In particular, we model and analyze the interaction among the data center operator and tenants by using Nash bargaining theory, and derive the optimal solutions for the load reduction and reimbursement for each tenant under two different bargaining protocols (i.e., sequential bargaining and concurrent bargaining). We prove that the derived solutions are Pareto-efficient and fair, and therefore self-enforcing and satisfactory for all entities. Numerical results based on trace-driven simulations show that the proposed bargaining approach is beneficial to both the data center operator and tenants, while also reducing the carbon emissions to the environment from data center demand response.

Who gets my flex? An evolutionary game theory analysis of flexibility market dynamics

Maintaining a real time balance between energy consumption and production is challenging when faced with increasing penetration of Renewable Energy Sources (RES) because of the increased variability in generation output. Demand-Side Management (DSM) techniques address this issue by steering consumers’ energy off-take, thereby enabling further penetration of RES. Present paper addresses the problem of overproduction from distribution grid connected wind generation. We present and analyze two business cases in the Belgian-European energy landscape for using upward consumption flexibility to deal with excessive wind power injection. We focus on the perspective of the flexibility providers and the strategic choice they face in choosing the business partner that maximizes their expected financial compensation. Evolutionary game theory is used to model this strategic choice and to provide a framework for quantifying realistic financial compensation bounds based on real world market and wind production data for multiple locations in Belgium. Results show that in a competitive market setting compensation payments for flexible power consumption are higher when dealing with higher wind forecast error levels. These results validate the economic benefits of having accurate wind production forecasts.

Critical kick-back mitigation through improved design of demand response

The energy sector is adopting a lot of intermittent renewable energy sources nowadays. In order to successfully integrate these renewable sources, demand side resources (DSR), in a demand response (DR) setup, are able to provide power system services by exploiting their flexibility in power consumption. Load kick-back effect describes a dynamic process that the total power consumption of a population of DSRs is higher than the expected value during the steady state after the activation of DR program, due to their temporary synchronous behaviors. For DR programs designed with little consideration of load kick-back, not only the potential value of DR is limited significant but also power system operation can be jeopardized even more. In addition to explaining the severity of kick-back effect through illustrative examples, this paper proposes several methods to mitigate the critical kick-back effect in DR while maintaining the expected value of DR. The proposed methods are applied to a DR program that aims at using thermostatically controlled heating of residential houses for peak shaving. Quality measures are adopted to measure the performance.

Embedded Implementation of VHR Satellite Image Segmentation.

Processing and analysis of Very High Resolution (VHR) satellite images provide a mass of crucial information, which can be used for urban planning, security issues or environmental monitoring. However, they are computationally expensive and, thus, time consuming, while some of the applications, such as natural disaster monitoring and prevention, require high efficiency performance. Fortunately, parallel computing techniques and embedded systems have made great progress in recent years, and a series of massively parallel image processing devices, such as digital signal processors or Field Programmable Gate Arrays (FPGAs), have been made available to engineers at a very convenient price and demonstrate significant advantages in terms of running-cost, embeddability, power consumption flexibility, etc. In this work, we designed a texture region segmentation method for very high resolution satellite images by using the level set algorithm and the multi-kernel theory in a high-abstraction C environment and realize its register-transfer level implementation with the help of a new proposed high-level synthesis-based design flow. The evaluation experiments demonstrate that the proposed design can produce high quality image segmentation with a significant running-cost advantage.

Model predictive control technologies for efficient and flexible power consumption in refrigeration systems

Considerable amounts of energy are consumed in supermarket refrigeration systems worldwide. Due to the thermal capacity of refrigerated goods and the rather simplistic control most commonly applied, there is a potential for distributing the system load over time in a more cost-optimal way. In this paper we describe a novel economic-optimizing Model Predictive Control (MPC) scheme that reduces operating costs by utilizing the thermal storage capabilities. A nonlinear optimization tool to handle a non-convex cost function is utilized for simulations with validated scenarios. In this way we explicitly address advantages from daily variations in outdoor temperature and electricity prices. Secondly, we formulate a new cost function that enables the refrigeration system to contribute with ancillary services to the balancing power market. This involvement can be economically beneficial for the system itself, while crucial services can be delivered to a future flexible and intelligent power grid (Smart Grid). Furthermore, we discuss a novel incorporation of probabilistic constraints and Second Order Cone Programming (SOCP) with economic MPC. A Finite Impulse Response (FIR) formulation of the system models allows us to describe and handle model as well as prediction uncertainties in this framework. This means we can demonstrate means for robustifying the performance of the controller.

Congestion Management in a Smart Grid via Shadow Prices

Abstract We consider a distribution grid interconnecting a number of consumers with flexible power consumption. Each consumer is under the jurisdiction of exactly one balancing responsible party (BRP), who buys energy at a day-ahead electricity market on behalf of the consumer. We illustrate how BRPs can utilize the flexibility of the consumers to minimize the imbalance between the consumed and the purchased energy thereby avoiding trading balancing energy at unfavorable prices. Further we show how shadow prices on the distribution lines can be used to resolve grid congestion without information sharing between the BRPs.

Design of Field Irrigation Multi-purpose Control Device Based on Idle Work Compensation

in irrigation of a farm well, the power factor of the load is low when the motor operates, because of the long low voltage lines. Long-term use will cause a lot of energy waste. To this question, this paper presents a new type intelligent controller to save irrigation water and energy. The principle of the new type field irrigation intelligent controller that based on AT89LV52 single chip is introduced in this paper. The integration of the low pressure electrical energy reactive compensation control and IC card prepaid multi-user three-phase watt-hour meter management is realized in the system. The energy conservation control, using the low pressure electrical energy reactive compensation, and prepaid multi-user three-phase watt-hour meter are used to realize the saving water management. Who inserts the card who irrigates. The design of hardware circuit, software flow, and experiment results are presented in detail. The results of testing and preproduction in Zibo Billion Electron Co., Ltd show that the design technique of integration controller is novel, and the system has the characteristics of controlling efficiency by reactive compensation, accurate measurement of electrical energy, flexible power consumption management with IC card prepaid multi-users, saving water , low cost, and so on. Therefore, it is especially suitable for power consumption and water resources management in rural well irrigation. The new type intelligent controller can effectively reduce the cost of irrigation and promote rural economic development.