Bei Han

Market-Based Control in Emerging Distribution System Operation

In emerging electrical distribution systems, a multitude of self-interested individual decision makers interacts among themselves and with the power grid. The optimal operation of the grid, according to a set of predefined technical and economic targets, can be achieved by influencing the behaviors of the decision makers with appropriate market signals. The technical feasibility and performance of the system, for example, in terms of line flow limits, network losses, and appropriate voltage profile, can thus be controlled to a certain extent, by market signals. In this paper, we present a conceptual framework for “Market-based Control” for the operation of emerging distribution systems. Characterized by distributed and adaptive control signals over prosumers, market-based control needs to make prosumer benefits aligned with regulator/DSOs concerns, thus satisfying the requirements from both sides. By applying market-based control in network charging, both network and market performances can be improved. The complexity in the environment and in the interactions among players prompt techniques to be derived from complex systems theory. A multiagent model was built up for testing the market control strategies strategy. The concept and applications are illustrated with reference to a standard CIGRE medium-voltage distribution network.

A Versatile Probability Model of Photovoltaic Generation Using Pair Copula Construction

Photovoltaic (PV) generation is increasingly popular in power systems. The nonlinear dependence associated with a large number of distributed PV sources adds the complexity to construct an accurate probability model and negatively affects confidence levels and reliability, thereby resulting in a more challenging operation of the systems. Most probability models have many restrictions when constructing multiple PV sources with complex dependence. This paper proposes a versatile probability model of PV generation on the basis of pair copula construction. In order to tackle the computational burden required to construct pair copula in high-dimensional cases, a systematic simplification technique is utilized that can significantly reduce the computational effort while preserving satisfactory precision. The proposed method can simplify the modeling procedure and provide a flexible and optimal probability model for the PV generation with complex dependence. The proposed model is tested using a set of historical data from colocated PV sites. It is then applied to the probabilistic load flow (PLF) study of the IEEE 118-bus system. The results demonstrate the effectiveness and accuracy of the proposed model.

Paths Toward Smart Energy: A Framework for Comparison of the EU and China Energy Policy

National decisions and behaviors are strictly interdependent and each of them may affect the whole planet; hence, the harmonization and coordination of the policy are the key issues. This is particularly true in the energy sector, where scarce resources, which are presently unevenly shared by the various nations with the possibility of conflicts arising need to be allocated to keep the pace with economic growth and in a manner compatible with the preservation of the environment. In this paper, we use the concept of “smart energy” as a way to embrace the target to meet commitments to the worlds sustainability, and a comparison of the energy policies of two key-world players, the EU and China, is undertaken in this context. A framework for quantitatively assessing the effectiveness of various specific policy tools is proposed. The policies are defined and analyzed with reference to the general goals, the tools to pursue those goals and their outcomes, possibly in a quantitative manner resorting to a set of meaningful metrics.

Smart energy grids and complexity science

This report proposes ideas and an approach to address present and future challenges in future smart energy systems through the particular lenses of complexity sciences. Complexities arising inside and around emerging energy distribution systems prompt a multilayered and integrated approach in which different disciplines and areas of expertize are pooled together. The interfaces between system layers and intellectual disciplines are the focus, rather than the details of any individual layer or the particularities of one approach. A group of people sharing this view and willing to proceed in this way organized a workshop at the Joint Research Centre of the European Commission, Petten, The Netherlands on 24 June 2012. Experts from different field of expertize convened to present their current research and discuss the future challenges of emerging smart energy systems via the afore-mentioned perspectives. z LD -N A-2626-EN -N As the Commission’s in-house science service, the Joint Research Centre’s mission is to provide EU policies with independent, evidence-based scientific and technical support throughout the whole policy cycle. Working in close cooperation with policy Directorates-General, the JRC addresses key societal challenges while stimulating innovation through developing new standards, methods and tools, and sharing and transferring its know-how to the Member States and international community. Key policy areas include: environment and climate change; energy and transport; agriculture and food security; health and consumer protection; information society and digital agenda; safety and security including nuclear; all supported through a cross-cutting and multi-disciplinary approach.

Partial Discharge Detection and Recognition in Random Matrix Theory Paradigm

The detection and recognition of partial discharge (PD) is an important topic in insulation tests and diagnoses. Take advantage of the affluent results from random matrix theory (RMT), such as eigenvalue analysis, M-P law, the ring law, and so on, a novel methodology in RMT paradigm is proposed for fast PD pulse detection in this paper. Furthermore, a scheme of time series modeling as random matrix is also proposed to extend RMT for applications with non-Gaussian noise context. Based on that, the eigenvalue distribution property is used for PD pattern recognition, which is completely new compared with traditional phase resolved PD and time-resolved PD methods. The simulation and experimental results show that the proposed methods are efficient, reliable, and feasible for PD detection and recognition especially for online applications.

Network Hierarchy Evolution and System Vulnerability in Power Grids

The seldom addressed network hierarchy property and its relationship with vulnerability analysis for power transmission grids from a complex-systems point of view are given in this paper. We analyze and compare the evolution of network hierarchy for the dynamic vulnerability evaluation of four different power transmission grids of real cases. Several meaningful results suggest that the vulnerability of power grids can be assessed by means of a network hierarchy evolution analysis. First, the network hierarchy evolution may be used as a novel measurement to quantify the robustness of power grids. Second, an antipyramidal structure appears in the most robust network when quantifying cascading failures by the proposed hierarchy metric. Furthermore, the analysis results are also validated and proved by empirical reliability data. We show that our proposed hierarchy evolution analysis methodology could be used to assess the vulnerability of power grids or even other networks from a complex-systems point of view.

Framework of Random Matrix Theory for Power System Data Mining in a Non-Gaussian Environment

A novel empirical data analysis methodology based on the random matrix theory (RMT) and time series analysis is proposed for the power systems. Among the ongoing research studies of big data in the power system applications, there is a strong necessity for new mathematical tools that describe and analyze big data. This paper used RMT to model the empirical data which also treated as a time series. The proposed method extends traditional RMT for applications in a non-Gaussian distribution environment. Three case studies, i.e., power equipment condition monitoring, voltage stability analysis and low-frequency oscillation detection, illustrate the potential application value of our proposed method for multi-source heterogeneous data analysis, sensitive spot awareness and fast signal detection under an unknown noise pattern. The results showed that the empirical data from a power system modeled following RMT and in a time series have high sensitivity to dynamically characterized system states as well as observability and efficiency in system analysis compared with conventional equation-based methods.

Smart Grid as Multi-layer Interacting System for Complex Decision Makings

This chapter presents an approach to the analysis of Smart Grids based on a multi-layer representation of their technical, cyber, social and decision-making aspects, as well as the related environmental constraints. In the Smart Grid paradigm, self-interested active customers (prosumers), system operators and market players interact among themselves making use of an extensive cyber infrastructure. In addition, policy decision makers define regulations, incentives and constraints to drive the behavior of the competing operators and prosumers, with the objective of ensuring the global desired performance (e.g. system stability, fair prices). For these reasons, the policy decision making is more complicated than in traditional power systems, and needs proper modeling and simulation tools for assessing “in vitro” and ex-ante the possible impacts of the decisions assumed. In this chapter, we consider the smart grids as multi-layered interacting complex systems. The intricacy of the framework, characterized by several interacting layers, cannot be captured by closed-form mathematical models. Therefore, a new approach using Multi Agent Simulation is described. With case studies we provide some indications about how to develop agent-based simulation tools presenting some preliminary examples.

A novel control method based on consensus algorithm for microgrids

Distributed control methods generally implemented with droop control have been widely applied in the energy management of microgrids. However, the robustness and stability of distributed control has been influenced by the size and diversity of the microgrid structure and compositions. Meanwhile, centralized control has been considered lack of flexibility and expansibility. In this paper, a novel control method based on consensus algorithm is proposed for improving current distributed energy management of microgrids. Taking advantages from distributed droop control framework, which would maintain a flexible and reliable microgrid with a manageable communication network, but also respond to the disturbance quickly, the proposed consensus based control method can enhance the speed and stability of distributed energy management. Simulations of hybrid AC-DC microgrids are also developed for the validation of theoretical analysis and effectiveness of the proposed method.

Probabilistic small signal analysis considering wind power correlation

The increasing integration of wind power generation brings more uncertainty into the power system. Since the correlation may have a notable influence on the power system, the output powers of wind farms are generally considered as correlated random variables in uncertainty analysis. In this paper, the pair copula theory is introduced to describe the complicated dependence of multidimensional wind power injection, and samples obeying this dependence structure are generated. Monte Carlo simulation is performed to analyze the small signal stability of a test system. The probabilistic stability under different correlation models is investigated. The results indicate that the probabilistic small signal analysis adopting pair copula model is more accurate and stable than other dependence models under different operating conditions.