Grid Optimization Research Articles

Research on improved artificial bee colony algorithm based on large-scale multi-objective group combination optimization

Large power system contains many generating units, and many factors need to be considered during operation. The unit combination optimization is a nonlinear large-scale optimization problem with multi-objective and multi-constraints, and the existing methods have many shortcomings. Artificial bee colony algorithm has good performance in solving nonlinear optimization problems, but it has some disadvantages such as low optimization efficiency and local extremum. To solve these problems, an improved artificial bee colony algorithm is proposed. The algorithm introduces variable field of vision, adjusts the initial solution and selection strategy, and combines with mutation operation in genetic algorithm. A multi - objective optimization model considering economy and environmental protection is constructed. In order to solve the problem that the calculation time is too long due to the expansion of the unit scale, a phased optimization method is adopted, and the improved algorithm is applied to the start-stop scheduling stage. After determining the start-stop state of the unit, the mixed integer programming method is used for load distribution. For unit contains up to 1000 units of large power grid optimization examples are simulated experiment, the experimental results show that the improved optimization algorithm convergence and global search ability is improved, greatly reduces the computation time of the mass unit combination, multiple objective conditions have also made the ideal results, verify the effectiveness of the proposed method.

Leveraging the Influence of Power Grid Links in Renewable Energy Power Generation

Smart grid construction provides the basic conditions for grid connection of renewable energy power generation. However, the grid connection of large-scale intermittent renewable energy sources increases the complexity of operational control of power systems. With the increase in intermittent renewable energy grid connections, distribution system operators must optimize and integrate these new participants to ensure the flexibility and stability of smart grids. Dividing the smart grid into logical clusters helps to overcome the problems caused by intermittent renewable energy grid connections. In this study, we propose a 2-step modeling approach that includes both link and leverage analyses to detect the network partitioning and assess the stability of the smart grids. Our experimental results of the link analysis show that, despite the identical scores in modularity and Silhouette Coefficients (SC), the total computational time of the linear programming model for linkage (CD1) is 29.8% shorter than that of the quadratic programming model for linkage (CD2) on 7 networks with fewer than 200 nodes, whereas CD2 is 29.5% faster than CD1 on 19 larger networks with more than 200 nodes. The leverage results of benchmark networks indicate that the computational time of each instance with the proposed linear programming model for leverage (ID1) and quadratic programming model for leverage (ID2) was substantially reduced, and the Critical Node Problem (CNP) results of medium- and large-scale networks were better than those reported in the literature, which play a significant role in smart grid optimization.

Distributed Demand Side Management With Stochastic Wind Power Forecasting

In this article, we propose a distributed demand-side management (DSM) approach for smart grids taking into account uncertainty in wind power forecasting. The smart grid model comprehends traditional users as well as active users (prosumers). Through a rolling-horizon approach, prosumers participate in a DSM program, aiming at minimizing their cost in the presence of uncertain wind power generation by a game theory approach. We assume that each user selfishly formulates its grid optimization problem as a noncooperative game. The core challenge in this article is defining an approach to cope with the uncertainty in wind power availability. We tackle this issue from two different sides: by employing the expected value to define a deterministic counterpart for the problem and by adopting a stochastic approximated framework. In the latter case, we employ the sample average approximation (SAA) technique, whose results are based on a probability density function (PDF) for the wind speed forecasts. We improve the PDF by using historical wind speed data, and by employing a control index that takes into account the weather condition stability. Numerical simulations on a real data set show that the proposed stochastic strategy generates lower individual costs compared to the standard expected value approach.

Tuning successive linear programming to solve AC optimal power flow problem for large networks

Successive linear programming (SLP) is a practical approach for solving large-scale nonlinear optimization problems. Alternating current optimal power flow (ACOPF) is no exception, particularly the large size of real-world networks. However, in order to achieve tractability, it is essential to tune the SLP algorithm presented in the literature. This paper presents a modified SLP algorithm to solve the ACOPF problem, specified by the U.S. Department of Energy’s (DOE) Grid Optimization (GO) Competition Challenge 1, within strict time limits. The algorithm first finds a near-optimal solution for the relaxed problem (i.e., Stage 1). Then, it finds a feasible solution in the proximity of the near-optimal solution (i.e., Stage 2 and Stage 3). The numerical experiments on test cases ranging from 500-bus to 30,000-bus systems show that the algorithm is tractable. The results show that our proposed algorithm is tractable and can solve more than 80% of test cases faster than the well-known Interior Point Method while significantly reduce the number of iterations required to solve ACOPF. The number of iterations is considered an important factor in the examination of tractability which can drastically reduce the computational time required within each iteration.

Development of Planning and Operation Guidelines for Strategic Grid Planning of Urban Low-Voltage Grids with a New Supply Task

In contrast to rural distribution grids, which are mostly “feed-in oriented” in terms of electrical power, urban distribution grids are “load oriented”, as the number of customer connections and density of loads in urban areas is significantly higher than in rural areas. Taking into account the progressive electrification of the transport and heating sector, it is necessary to assess the required grid optimization or expansion measures from a conventional, as well as an innovative point of view. This is necessary in order to be able to contain the enormous investment volumes needed for transforming the energy system and aligning the infrastructures to their future requirements in time. Therefore, this article first explains the methodological approach of allocating scenarios of the development of electric mobility and heat pumps to analyzed grids. The article continues with describing which power values need to be applied and which conventional and innovative planning measures are available for avoiding voltage band violations and equipment overloads within the framework of strategic grid planning. Subsequently, the results of grid planning studies are outlined and evaluated with an assessment model that evaluates capital as well as operational costs. On this basis, planning and operation guidelines for urban low-voltage grids are derived. The main result is that low-voltage grids can accommodate charging infrastructure for electric mobility, as well as heat pumps to a certain degree. In addition, it is concluded that conventional planning measures are not completely avoidable, but can be partially avoided or deferred through dynamic load management.

Linear solvers for power grid optimization problems: A review of GPU-accelerated linear solvers

The linear equations that arise in interior methods for constrained optimization are sparse symmetric indefinite, and they become extremely ill-conditioned as the interior method converges. These linear systems present a challenge for existing solver frameworks based on sparse LU or LDLT decompositions. We benchmark five well known direct linear solver packages on CPU- and GPU-based hardware, using matrices extracted from power grid optimization problems. The achieved solution accuracy varies greatly among the packages. None of the tested packages delivers significant GPU acceleration for our test cases. For completeness of the comparison we include results for MA57, which is one of the most efficient and reliable CPU solvers for this class of problem.

ε-constraint guided stochastic search with successive seeding for multi-objective optimization of large-scale steel double-layer grids

This paper proposes a design-driven structural optimization algorithm named ε-constraint guided stochastic search (ε-GSS) for multi-objective design optimization of large-scale steel double-layer grids having numerous discrete design variables. Based on the well-known ε-constraint method, first, the multi-objective optimization problem is transformed into a set of single-objective optimization problems. Next, each single-objective optimization problem is tackled using an enhanced reformulation of the standard guided stochastic search algorithm proposed based on a stochastic maximum incremental/decremental step size approach. Moreover, a successive seeding strategy is employed in conjunction with the proposed ε-GSS algorithm to improve its performance in multi-objective optimization of large-scale steel double-layer grids. The numerical results obtained through multi-objective optimization of three challenging test examples, namely a 1728-member double-layer compound barrel vault, a 2304-member double-layer scallop dome, and a 2400-member double-layer multi-radial dome, demonstrate the usefulness of the proposed ε-GSS algorithm in generating Pareto fronts of the foregoing multi-objective structural optimization problems with up to 2400 distinct sizing variables.

An Overall Uniformity Optimization Method of the Spherical Icosahedral Grid Based on the Optimal Transformation Theory

The improvement of overall uniformity and smoothness of spherical icosahedral grids, the basic framework of atmospheric models, is a key to reducing simulation errors. However, most of the existing grid optimization methods have optimized grid from different aspects and not improved overall uniformity and smoothness of grid at the same time, directly affecting the accuracy and stability of numerical simulation. Although a well-defined grid with more than 12 points cannot be constructed on a sphere, the area uniformity and the interval uniformity of the spherical grid can be traded off to enhance extremely the overall grid uniformity and smoothness. To solve this problem, an overall uniformity and smoothness optimization method of the spherical icosahedral grid is proposed based on the optimal transformation theory. The spherical cell decomposition method has been introduced to iteratively update the grid to minimize the spherical transportation cost, achieving an overall optimization of the spherical icosahedral grid. Experiments on the four optimized grids (the spring dynamics optimized grid, the Heikes and Randall optimized grid, the spherical centroidal Voronoi tessellations optimized grid and XU optimized grid) demonstrate that the grid area uniformity of our method has been raised by 22.60% of SPRG grid, −1.30% of HR grid, 38.30% of SCVT grid and 38.20% of XU grid, and the grid interval uniformity has been improved by 2.50% of SPRG grid, 2.80% of HR grid, 11.10% of SCVT grid and 11.00% of XU grid. Although the grid uniformity of the proposed method is similar with the HR grid, the smoothness of grid deformation has been enhanced by 79.32% of grid area and 24.07% of grid length. To some extent, the proposed method may be viewed as a novel optimization approach of the spherical icosahedral grid which can improve grid overall uniformity and smoothness of grid deformation.

Modeling, cost optimization and management of grid connected solar powered charging station for electric vehicle

Abstract Transportation sector is currently the largest carbon emitter and produces lots of harmful gases in the atmosphere, as a result Electric Vehicle (EV) is an attractive choice for mankind. Increased use of EVs needs the establishment of a large number of EV charging stations. This paper shows comprehensive assessment of solar powered charging station which is used for the charging of electric vehicles. This paper includes the design, cost optimization and management of an EV charging station along with pre-feasibility analysis for location assessment for EV charging stations. A case study for Ahmedabad is presented in this paper. Solar-Pro software is used for design, technical and financial assessment of solar powered based charging stations. Memetic and Cuckoo optimization techniques are used for cost optimization. Further Program, Evaluation and Review Technique (PERT) is used to assess the cradle to grave process of charging stations and at the end strengths, weaknesses, opportunities and threats (SWOT) and Political, Economic, Social, Legal, Environmental (PESTLE) analysis is presented, which shows strength and weakness of project from political, economical, social and ecological points of view. Results shows that Ahmedabad is suitable location for the solar powered charging station due to the mean value and maximum value of solar radiation is 5.7516 and 10.8869 kwh/m2/day respectively. The available energy of the given system is 30,506 kwh/m2/day at the solar fraction of 97.12%. The result of cuckoo algorithm for cost optimization is much better than memtic algorithm. Result also shows the reliability of whole system is 89%.

Towards online optimization for power grids

In this note, we discuss potential advantages in extending distributed optimization frameworks to enhance support for power grid operators managing an influx of online sequential decisions. First, we review the state-of-the-art distributed optimization frameworks for electric power systems, and explain how distributed algorithms deliver scalable solutions. Next, we introduce key concepts and paradigms for online optimization, and present a distributed online optimization framework highlighting important performance characteristics. Finally, we discuss the connection and difference between offline and online distributed optimization, showcasing the suitability of such optimization techniques for power grid applications.

Pole-Curb Fusion Based Robust and Efficient Autonomous Vehicle Localization System With Branch-and-Bound Global Optimization and Local Grid Map Method

The localization system is one of the key components of autonomous vehicles. Widely-used GNSS (low-cost) alone can not meet the centimeter accuracy and is unstable in urban environment. Typically LiDAR-based localization system is very computationally demanding due to the usage of the dense point cloud map. In this paper, we propose a novel lightweight LiDAR-based localization system for autonomous vehicle in this paper. The proposed system only relies on lightweight poles and curbs landmark map, which is highly robust and efficient compared to other localization systems. Poles and curbs are selected as landmarks because of their commonality and stability. We novelly propose a Branch-and-Bound (BnB)-based global optimization method to tackle the data association problem of poles. Motion decoupling is adopted to decouple translation and rotation to improve the efficiency of the BnB-based algorithm. Besides, we propose a new local grid map-based representation for curbs to make better use of curb information. Cost functions for pole and curb are defined respectively and then fused for the subsequent non-linear optimization method to obtain the vehicle location. Experiments on KITTI dataset and our self-collected dataset demonstrate the efficiency and accuracy of our system.

Distributed generation parameter optimization method based on fuzzy C-means clustering under the Internet of Things architecture

Aiming at the problem that it is difficult to maximize market profits when various types of distributed generation work together in the power grid, this paper proposes a distributed generation parameter optimization method based on Internet of Things and fuzzy C-means clustering. Firstly, a grid distributed generation model is established under the Internet of Things architecture, and its internal parameter relationships are determined correspondingly. Secondly, the comprehensive evaluation criteria and constraints of distributed generation are proposed for maximizing market benefits. Finally, the distributed communication model based on Internet of Things and fuzzy C-means clustering are adopted to achieve the adaptive optimization of grid distributed generation parameters. The experiment simulation is carried out with IEEE standard test system. The results show that the total load and total revenue of the proposed algorithm are increased by 8.41% and 14.66% respectively compared with the traditional optimization algorithm considering generation side benefits and the algorithm considering energy loss only. Experimental results fully demonstrate the effectiveness of proposed algorithm.

Smart Grid Technologies as a Concept of Innovative Energy Development: Initial Proposals for the Development of Ukraine

The formation of the concept of Smart Grid is associated with a number of issues, including theoretical and methodological. One of the main problems in forming such theory is to build its basis, the starting point for the development of which is the definition of Smart Grid as a systems of views concept on the future of power engineering, the principles of operation and technological basis of which undergoes significant changes compared to modern energy. The paper is aimed at reviewing and developing directions and approaches to the definition of Smart Grid in combination with machine learning mechanisms, highlighting their diverse and common nature to develop a holistic innovative energy development. In this paper, a study of the energy sector in Ukraine was conducted. Its efficiency and innovative development are considered. Problems with the implementation of Smart Grid technology, which arise when using alternative sources and monitoring and administration systems, were highlighted. The ways of mathematical formulation of the Smart Grid optimization problem are determined using the Data Science approach based on the machine learning system and neural networks. Big Data processing methods, Data Mining, statistical methods, artificial intelligence methods, and Machine Learning are analyzed. The design and development of databases and application software will be done using the Data Science method. Smart-technologies will take over the processes of control, accounting and diagnostics of assets, which will provide promising opportunities for self-recovery of the power system, as well as efficient operation of fixed assets. With the introduction of Smart Grid technologies for the Ukrainian power industry, significant fundamental changes will take place. This is the transition from centralized methods of generation and transmission of electricity to distributed networks with the ability to control energy production facilities and network topology at any point, including at the consumer level. Replacement of centralized demand forecasting according to the methodology of active consumer influence becomes an element and subject of the management system. A high-performance information and computing infrastructure will be built as the core of the energy system. This approach creates the preconditions for the widespread introduction of new devices that increase the maneuverability and controllability of the equipment. The creation of next-generation operational applications (SCADA/EMS/NMS) allows the use of innovative algorithms and methods of power system management, including its new active power elements.

A variable neighborhood search simheuristic algorithm for reliability optimization of smart grids under uncertainty

AbstractA fundamental part of the self‐healing grid is reducing disturbances and automatically responding to problems. Toward this goal, smart grid operators would be of great use to ensure stability after a potential overload for a planning horizon, as the electrical values are unknown. To evaluate the robustness of the topology reconfiguration after a disturbance, like an overload, reliability analysis through simulation can be employed. However, the simulation approach we propose in this paper, except for the excessive time it consumes, cannot assure the quality of its solutions. To achieve a more robust configuration, we, additionally, propose a single‐stage stochastic program to optimize the grid topology configuration after a potential overload to ensure stability for the next day. We suggest a simheuristic approach based on a Variable Neighborhood Search metaheuristic to solve the above stochastic optimization problem. We evaluate the two approaches for this real‐world problem, together with Creos Luxembourg S.A., the leading grid operator in Luxembourg. We show that our method can quickly suggest countermeasures to operators facing potential overloading incidents, ensuring the smart grid's stability for the next day.

Digital and automatic design of free-form single-layer grid structures

Grid shells have been widely used in various long-span public buildings, and many of them are defined over free-form surfaces with complex boundaries. This emphasizes the importance of general and digitalised grid generation and optimization methods in the initial design stage to achieve visually sound grid shells. In this paper, a framework is presented for the development of a digital tool and to generate regular and fluent grids for structural design over free-form surfaces, especially those with complex boundaries. Both triangular and quadrilateral grid generation are addressed. To generate regular and fluent grids for free-form surfaces, a simple yet practical framework is proposed based on a spring-mass model. Firstly, an initial casual quadrilateral grid is tiled on the surface based on surface discretization and mesh parameterization. Secondly, the distribution of the initial grid vertices is adjusted by a dynamic relaxation procedure, assuming the grid as a spring-mass system. Thirdly, the grid vertices corresponding to the adjusted particles in the equilibrium state are then reconnected to produce a grid with a predefined pattern (triangular or quadrilateral). Finally, the generated grid is relaxed with the spring-mass model, alongside additional geometric operations including grid size adjustment and filtering techniques, to further improve the grid regularity and fluency. As part of its contribution, this paper also broadens the application scope of the fluency index, which can be used to quantitatively evaluate the suitability of a given triangular or quadrilateral grid for architectural and structural expression. Examples are presented and show that the proposed framework is effective for the triangular and quadrilateral grid generation over various surfaces and to optimize the resulted grids along complex boundaries. The method proposed can be useful for rapid design and performance evaluation of free-form grid structures.

Customer Load Forecasting Method Based on the Industry Electricity Consumption Behavior Portrait

With the dramatic increase of energy demand and the continuous increase of power system operation pressure, higher requirements are put forward for the development of power grid planning and optimization operation. It is important for the refinement of distribution network planning to deeply extract the characteristics of user load. First, the process of load characteristic analysis method from the user level to the industry level is proposed, which achieves the division of electricity consumption patterns of various industries, thus building a panoramic portrait of industry electricity consumption behavior. Then, by expanding the information filled in by traditional customers, the feature vector of each user is extracted, and the users' industry electricity consumption patterns are used as the label. Therefore, a method for identifying the electricity consumption pattern of the customer based on the BB-stacking model fusion framework is proposed, which yields the preliminary forecast results of customer load based on the actual load accounting results of the customers. Finally, comparative simulations with different methods verify the effectiveness of the proposed algorithm, which can provide prominent guidance for the actual distribution network planning work.

Data-driven stochastic optimization for power grids scheduling under high wind penetration

To address the environmental concern and improve the economic efficiency, the wind power is rapidly integrated into smart grids. However, the inherent uncertainty of wind energy raises operational challenges. To ensure the cost-efficient, reliable and robust operation, it is critically important to find the optimal decision that can correctly and rigorously hedge against all sources of uncertainty. In this paper, we propose data-driven stochastic unit commitment (SUC) to guide the power grids scheduling. Specifically, given the finite historical data, the posterior predictive distribution is developed to quantify the wind power prediction uncertainty accounting for both inherent stochastic uncertainty of wind power generation and input model estimation error. For complex power grid systems, a finite number of scenarios is used to estimate the expected cost in the planning horizon. To further control the impact of finite sampling error induced by using the sample average approximation (SAA), we propose a parallel computing based optimization solution methodology, which can quickly find the reliable optimal unit commitment decision hedging against various sources of uncertainty. The empirical study over six-bus and 118-bus systems demonstrates that our approach can provide more cost-efficient and robust performance than the existing deterministic and stochastic unit commitment approaches.

A fully nonlinear potential flow wave modelling procedure for simulations of offshore sea states with various wave breaking scenarios

An accurate representation of a given sea state is crucial for the study of hydrodynamic loads on offshore structures. It is straightforward to check the quality of the reproduced regular waves in a numerical wave tank (NWT). However, many more parameters need to be considered to ensure the quality of irregular waves. In this paper, a fully non-linear potential flow (FNPF) wave model is used to reproduce irregular sea states with different severity of wave breaking. The numerical model solves the velocity potential from the Laplace equation and the free surface boundary conditions using a finite difference method on a σ-coordinate grid. A comprehensive procedure is introduced to ensure the quality of the reproduced full-scale sea states. The effect of wave spectrum discretisation techniques and breaking wave algorithms are compared for an optimal performance. The evaluation of the simulation results takes into account the kurtosis and the wave crest distribution in addition to the wave spectra. The vertical arrangement of the σ-coordinate grid plays an important role in representing the dispersion relation and a grid optimisation method is explained. The current study provides a working procedure that reproduces high-fidelity irregular sea states with breaking waves in an efficient FNPF NWT.

Voltage and reactive power correlation in multi-objective optimization of an offshore grid

In the last few years, many offshore wind power plants are installed at North Sea and more are under construction. Offshore grid would be vital in the integration of future offshore wind power plant with the main land grid. Both, multi-terminal DC and AC cable systems are under consideration in the concept of future offshore grid. The reactive power and voltage operating point for such a network is important for the optimized operation. This article presents an optimization criterion of voltage and reactive power control for an offshore AC grid having a parallel connecting grid-forming converter. Multi-objective optimization problem is formulated considering four reactive power management strategies. The solution of the optimization algorithm is analysed and compared with respect to active power losses in the network, voltage variation, and reactive power contribution by the sources. The research presents a methodology to apply a suitable reactive power management strategy to achieve the best optimum operating points. The solution provides the optimum operating points for offshore wind farm reactive power, frequency and voltage droop gain values for VSC-HVDC system, and reactive power sharing factor of HVDC transmission.

A dual-modality quantification of scattered radiation from head to female breasts during radiological investigations in a tertiary hospital in Nigeria

BackgroundTo quantify the amount of scattered radiation reaching the breasts during x-ray and CT investigations of the head in order to find appropriate justification for an intended change in practice involving torso shielding.ResultsScattered radiation from the head reached the breasts in both procedures. The range and mean dose were (CR 1.02–3.61/1.94 ± 0.63 mGy) and (CT 2.20–8.50/3.74 ± 2.28 mGy). Both breasts had enormous dose difference in CR (72.3%) and CT (51.4%) which were statistically significant (p < 0.05). Correlation of dose with anthropometric parameters gave weak results.ConclusionDespite dose mitigation strategies such as software and hardware modifications in radiological modalities, use of anti-scatter grid, appropriate collimation and dose optimization by radiographers, scattered radiation still traveled from the head to the breasts. These were, however, significantly reduced when shielding was applied. For a dose-safe practice, radiographers are urged, in addition to current strategies at mitigating scatters, to adopt torso shielding during examinations involving contiguous anatomies to the breast.