Large-scale Energy Management Research Articles

Comprehensive hydraulic performance improvement in a pump-turbine: An experimental investigation

Pumped storage power plants, which is known as only large-scale energy management equipment, plays a vital important role in energy field. Hump characteristic and S characteristic are two classical unsteady hydraulic characteristics when pump-turbine operating at turbine mode and pump mode, respectively. It has been found that these two characteristics are both strongly related to the complex vortex evolution process in vaneless region while only few papers focus on the elimination mechanism of them. In present paper, a scaled runner with optimized high-pressure side (HPS) is designed and manufactured based on multi-objective optimization process arming at eliminating unsteady characteristic, i.e. hump characteristic and S characteristic, while maintaining efficiency characteristic unchanged. Thereafter, hydraulic experiments are conducted to investigate the impact of HPS geometry on hydraulic performance (efficiency, hump margin, S margin and pressure fluctuation) of object pump turbine. The experimental results show that compared with the original runner, the weighted average efficiency for pump mode increases by 0.1% while the weighted average efficiency for turbine mode decreases by 0.1%. Moreover, the S margin increases from 76.7 m to 87.2 m and the S2 unsteady region is largely increased. The hump margin decreases a little while it can recover to the original level through increasing 0.6% of the original ratio scale. Moreover, the runner with optimized HPS can effectively reduce the pressure fluctuation amplitude in vaneless region up to 33.3% and 21.4% for turbine mode and pump mode, respectively.

Real-Time Machine Learning-based fault Detection, Classification, and locating in large scale solar Energy-Based Systems: Digital twin simulation

The current study considers numerous renewable energy resources, distributed power generation units, energy storage, and plug-in hybrid electric vehicles (PHEV) in order to propose a reliable large-scale energy management framework that can be applied to islanded and grid-connected operations of renewable hybrid AC-DC microgrids (MGs). The framework uses a bat optimization algorithm (BOA) for minimizing the operating costs of the network and in addition introduces an intrusion detection system (IDS) according to the sequential hypothesis testing (SHT) method for detecting identity-enabled cyber-attacks (i.e classification of Sybil attacks, masquerading attacks) on the wireless-enabled advanced metering infrastructures (AMI). The suggested IDS uses the received signal strength (RSS) amount for distinguishing various signal resources and detecting cyberattacks. An IEEE 33-bus testing system has been used to construct a real-time hybrid MG in order to determine the reliability and efficiency of the suggested framework.

Long-distance renewable hydrogen transmission via cables and pipelines

Intermittency is one of the main obstacles that inhibit the wide adoption of the renewable energy in the power sector. Small-scale fluctuations can be tackled by short-term energy storage system, whereas long-term or seasonal intermittencies rely on large-scale energy management solutions. Besides the supply and demand mismatch in temporal domain, renewable energy sources are usually far away from consumption points. To connect the energy sources to the demand cost-effectively, cable transmission is usually the default option, and considering the long distance, other emerging energy carriers such as hydrogen could be a feasible option. However, there is handful studies on the quantitative evaluation of the long-distance energy transmission cost. This paper investigated the economic feasibility of renewable energy transmission via routes of power cable and gas pipeline. In the direct power transmission case, renewable energy is transmitted via HVDC cable and then converted to hydrogen for convenient storage. The alternative case converts renewable energy into hydrogen at the source and transports the hydrogen in the gas pipeline to consumers. Existing data available from public domain are used for cost estimation. Results show that the improvements of capacity factor and transmission scale are the most cost-effective approach to make the renewable hydrogen economically viable. At 4000 km of transmission distance, renewable hydrogen LCOE of 7 US$/kg and 9 US$/kg are achievable for the corresponding optimum cases, respectively.

The application of building energy management system based on IoT technology in smart city

To introduce new energy management (EM) systems that apply solar energy, geothermal energy, and wind energy to intelligent building (IB), so as to reduce the energy consumption of traditional buildings, and integrate it into the building equipment management system (EMS) to make the application of new energy more transparent and rationalized. The research results show that according to the three characteristics of Internet of Things (IoT)—“Sensor, Internet, Intelligent”, the problem of data connection between IB EMS and IoT is solved. Then, through the user’s demand analysis of application services, it is determined the service content that the system can provide for the user. It can be concluded that the application development environment of the IoT data application platform is used to design and develop a new energy application management system based on the IoT, which lays a theoretical foundation for realizing the large-scale EM of “smart city”.

DABGEO: A reusable and usable global energy ontology for the energy domain

The heterogeneity of energy ontologies hinders the interoperability between ontology-based energy management applications to perform a large-scale energy management. Thus, there is the need for a global ontology that provides common vocabularies to represent the energy subdomains. A global energy ontology must provide a balance of reusability–usability to moderate the effort required to reuse it in different applications. This paper presents DABGEO: a reusable and usable global ontology for the energy domain that provides a common representation of energy domains represented by existing energy ontologies. DABGEO can be reused by ontology engineers to develop ontologies for specific energy management applications. In contrast to previous global energy ontologies, it follows a layered structure to provide a balance of reusability–usability. In this work, we provide an overview of the structure of DABGEO and we explain how to reuse it in a particular application case. In addition, the paper includes an evaluation of DABGEO to demonstrate that it provides a balance of reusability–usability.

DABGEO: A Reusable and Usable Global Energy Ontology for the Energy Domain

The heterogeneity of energy ontologies hinders the interoperability between ontology-based energy management applications to perform a large-scale energy management. Thus, there is the need for a global ontology that provides common vocabularies to represent the energy subdomains. A global energy ontology must provide a balance of reusability-usability to moderate the effort required to reuse it in different applications. This paper presents DABGEO: a reusable and usable global ontology for the energy domain that provides a common representation of energy domains represented by existing energy ontologies. DABGEO can be reused by ontology engineers to develop ontologies for specific energy management applications. In contrast to previous global energy ontologies, it follows a layered structure to provide a balance of reusability-usability. In this work, we provide an overview of the structure of DABGEO and we explain how to reuse it in a particular application case. In addition, the paper includes an evaluation of DABGEO to demonstrate that it provides a balance of reusability-usability.

Enabling Model-Based LTI for Large-Scale Power System Security Monitoring and Enhancement With Graph-Computing-Based Power Flow Calculation

The voltage stability is an essential security concern when a system is operating in peak load hours or subjected to an N-1 contingency. Among various voltage stability indices, Local Thevenin Index (LTI) has been a popular one, but mostly applied to the measurement-based framework. The reason is that it requires significant computing effort if applied to model-based approach, even though the model-based LTI calculation can provide more accurate results. In this paper, a new model-based LTI calculation is proposed using graph-computing-based power flow calculation, which is fast enough to fully utilize the advantage of model-based LTI. Then, when the voltage stability index of one or multiple buses exceeds the security limit, a demand response scheme is activated to increase the voltage security margin at the lowest cost. The simulation study of a thousand-level-bus system verifies the computation accuracy and efficiency of the proposed model-based LTI. It also demonstrates that the demand response action can enhance the system security effectively. Thus, the proposed work has advantages if applied to a large-scale Energy Management System (EMS).

A Novel Inexact Two-Stage Stochastic Robust-Compensation Model for Electric Supply Environmental Management Under Uncertainty

In this study, a novel inexact two-stage stochastic robust-compensation programming (ITSP-RC) model is developed for CO2 emission reduction management under uncertainties. This model is attempted to integrate ITSP and stochastic RC programming into a general framework and apply the ITSP-RC for power management and CO2 emission reduction management, such that the developed model can tackle uncertainties described in terms of interval values and probability distributions over a two-stage context. Moreover, it can reflect dynamic and randomness of the energy systems during the planning horizon. The developed method has been applied to a case to solve CO2 emission management problem in electric supply environmental management. A number of scenarios corresponding to different adoption rate levels of carbon capture, utilization, and storage technology are examined. With the RC programming, regional energy systems would have a stable financial budget. The result suggests that the methodology is applicable for reflecting complexities of large-scale energy management systems and addressing CO2 emissions reduction issue with the planning period.

Photovoltaic Burp Charge System on Energy-Saving Configuration by Smart Charge Management

To achieve pulse charge using direct photovoltaic (PV) energy, it is necessary to keep the energy supply from PV module continuous and optimum, especially to prevent energy supply from interrupting during the pulse break period. To perform the mentioned idea, a PV burp charge system (PV-BCS) proposed is to make PV energy pump continuous with optimum maximum power point tracking (MPPT), which increases the PV utilization, and to dispatch the remaining PV energy for further storage in the pulse break period. A smart charging management (CM) presents three kinds of charge statuses, a burp pulse (BP) charge and two pulse charges, which charges three batteries, Bm, B1, and B2 concurrently and individually. The two pulse charges provided are for accompanying the BP charge in order to keep the PV energy supply continuous and cherish the remaining energy for further storage. The BP charge for Bm is composed of a positive pulse (PP) charge in a positive burp pulse (PBP) period and negative pulse (NP) charge in non-PBP period. The other two batteries B1 and B2 are always with PP charge in the non-PBP period, in which B1 charges the remaining PV energy to keep the PV energy supply continuous, realizing the energy treasuring concept; B2 charges the discharge amount from Bm by intensely discharging, which indeed is equivalent to the NP charge, achieving the energy recovery concept. A laboratory prototype, 250-W PV-BCS, with elaborated simulation and experiment demonstrates the proposed concurrent charging idea, which is feasible for conceiving an energy-saving concept, especially applicable in a large-scale energy management for such as battery exchange station to electric vehicle service. In addition, the PV-BCS can be further as a hybrid charger for renewable energy application if additional renewable energy is mixed with the PV energy, such as wind energy.

A Hybrid Approach Combining Local Search and Constraint Programming for a Large Scale Energy Management Problem

This paper presents a heuristic approach combining constraint satisfaction, local search and a constructive optimization algorithm for a large-scale energy management and maintenance scheduling problem. The methodology shows how to successfully combine and orchestrate different types of algorithms and to produce competitive results. We also propose an efficient way to scale the method for huge instances. A large part of the presented work was done to compete in the ROADEF/EURO Challenge 2010, organized jointly by the ROADEF, EURO and Electricite de France. The numerical results obtained on official competition instances testify about the quality of the approach. The method achieves 3 out of 15 possible best results.

Exact and heuristic solutions for combinatorial optimization problems

This is a summary of the Ph.D. thesis defended by the author on July 2012 at the University of Modena and Reggio Emilia. The thesis was supervised by Mauro Dell’Amico and co-supervised by Manuel Iori. The manuscript is written in English and is available from the author upon request at jose.diaz@unimore.it. The thesis addresses relevant optimization problems: the Bin Packing Problem, the Quadratic Assignment Problem, the large-scale Energy Management Problem, and the Node, Edge and Arc Routing Problem. To solve these problems exact, heuristic and local search techniques are proposed. The first problem studied is the Bin Packing Problem with Precedence Constraints (BPP-P): given a set of identical capacitated bins, a set of weighted items and a set of precedences among those items, we are interested in determining the minimum number of bins that can accommodate all items and can be ordered in such a way that all precedences are satisfied. The problem has a very intriguing combinatorial structure and models many assembly and scheduling issues. According to our knowledge the BPP-P has received little attention in the literature, and in the thesis we address it for the first time with exact solution methods. In particular, we develop reduction criteria, a large set of lower bounds, a variable neighborhood search upper bounding technique and a branch-and-bound algorithm (see Dell’Amico M., Diaz Diaz J.C. and Iori M. (2012): The Bin Packing Problem with Precedence Constraints, Operations Research, doi:10.1287/opre.1120.1109). We show the effectiveness of the proposed algorithms by means of extensive computational tests on benchmark instances and comparison with standard integer linear programming techniques. The second problem studied is to find the Friendly Bin Packing Instances without Integer Round-up Property. It is well known that the gap between the optimal values of bin packing and fractional bin packing, if the latter is rounded up to the closest integer, is almost always null. Known counterexamples to this for integer input values involve fairly large numbers. Specifically, the first one was derived in 1986 and involved a bin capacity of the order of a billion. Later in 1998 a counterexample with a bin capacity of the order of a million was found. In the thesis we show a large number of counterexamples with bin capacity of the order of a hundred, showing that the gap may be positive even for numbers which arise in customary applications. The associated instances are constructed starting from the Petersen graph and taking advantage of the fact that it is fractionally, but not integrally, 3-edge colorable. The third problem studied is the Single-finger Keyboard Layout Problem. The problem of designing new keyboards layouts able to improve the typing speed of an

DEHEMS: creating a digital environment for large-scale energy management at homes

Located at the consumer-end of the Smart Grid, domestic energy monitoring and management systems aim to provide direct energy feedback whilst (or shortly after) consumption occurs, so as to persuade users to achieve energy saving and efficiency. However, existing solutions are challenged by the lack of large-scale practice and study on user behaviours and preferences. In this paper, we present a domestic energy management system (DEHEMS), which deploys electricity and gas monitoring in European-wide homes. The system has been developed in three cycles in order for households to participate and contribute. Results based on both qualitative and quantitative data analysis show that less energy has been consumed using the system. Additionally, positive behavioural changes have been achieved among households.

A solution approach based on Benders decomposition for the preventive maintenance scheduling problem of a stochastic large-scale energy system

This paper describes a Benders decomposition-based framework for solving the large scale energy management problem that was posed for the ROADEF 2010 challenge. The problem was taken from the power industry and entailed scheduling the outage dates for a set of nuclear power plants, which need to be regularly taken down for refueling and maintenance, in such a way that the expected cost of meeting the power demand in a number of potential scenarios is minimized. We show that the problem structure naturally lends itself to Benders decomposition; however, not all constraints can be included in the mixed integer programming model. We present a two phase approach that first uses Benders decomposition to solve the linear programming relaxation of a relaxed version of the problem. In the second phase, integer solutions are enumerated and a procedure is applied to make them satisfy constraints not included in the relaxed problem. To cope with the size of the formulations arising in our approach we describe efficient preprocessing techniques to reduce the problem size and show how aggregation can be applied to each of the subproblems. Computational results on the test instances show that the procedure competes well on small instances of the problem, but runs into difficulty on larger ones. Unlike heuristic approaches, however, this methodology can be used to provide lower bounds on solution quality.

Solving a real-life, large-scale energy management problem

This paper introduces a three-phase hybrid heuristic for a large-scale energy management and maintenance scheduling problem. The problem is to schedule maintenance periods and refueling amounts for nuclear power plants with a time horizon of up to five years, and handling a number of scenarios for future demand and prices. The goal is to minimize the expected total production cost. The first phase of the heuristic solves a constraint programming model of a simplified version of the problem, the second performs a local search, and the third handles overproduction in a greedy fashion. This work was initiated in the context of the ROADEF/EURO Challenge 2010. In the concluding phase of the competition, our team ranked second in the junior category and sixth overall. After correcting a small implementation bug in the program that was submitted for final evaluation, our solver ranks first in the overall results from the competition.

A constraint programming-based approach to a large-scale energy management problem with varied constraints

This paper addresses a large-scale power plant maintenance scheduling and production planning problem, which has been proposed by the ROADEF/EURO Challenge 2010. We develop two lower bounds for the problem: a greedy heuristic and a flow network for which a minimum cost flow problem has to be solved. Furthermore, we present a solution approach that combines a constraint programming formulation of the problem with several heuristics. The problem is decomposed into an outage scheduling and a production planning phase. The first phase is solved by a constraint program, which additionally ensures the feasibility of the remaining problem. In the second phase we utilize a greedy heuristic--developed from our greedy lower bound--to assign production levels and refueling amounts for a given outage schedule. All proposed strategies are shown to be competitive in an experimental evaluation.

Heterogeneous Collaborative Sensor Network for Electrical Management of an Automated House with PV Energy

In this paper we present a heterogeneous collaborative sensor network for electrical management in the residential sector. Improving demand-side management is very important in distributed energy generation applications. Sensing and control are the foundations of the “Smart Grid” which is the future of large-scale energy management. The system presented in this paper has been developed on a self-sufficient solar house called “MagicBox” equipped with grid connection, PV generation, lead-acid batteries, controllable appliances and smart metering. Therefore, there is a large number of energy variables to be monitored that allow us to precisely manage the energy performance of the house by means of collaborative sensors. The experimental results, performed on a real house, demonstrate the feasibility of the proposed collaborative system to reduce the consumption of electrical power and to increase energy efficiency.