Chance-Constrained Day-Ahead Hourly Scheduling in Distribution System Operation

This paper aims to propose a two-step approach for day-ahead hourly scheduling in a distribution system operation, which contains two operation costs, the operation cost at substation level and feeder level. In the first step, the objective is to minimize the electric power purchase from the day-ahead market with the stochastic optimization. The historical data of day-ahead hourly electric power consumption is used to provide the forecast results with the forecasting error, which is presented by a chance constraint and formulated into a deterministic form by Gaussian mixture model (GMM). In the second step, the objective is to minimize the system loss. Considering the nonconvexity of the three-phase balanced AC optimal power flow problem in distribution systems, the second-order cone program (SOCP) is used to relax the problem. Then, a distributed optimization approach is built based on the alternating direction method of multiplier (ADMM). The results shows that the validity and effectiveness method.Comment: 5 pages, preprint for Asilomar Conference on Signals, Systems, and Computers 201

Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions

Multiple Timescale Dispatch and Scheduling for Stochastic Reliability in Smart Grids with Wind Generation Integration

Integrating volatile renewable energy resources into the bulk power grid is challenging, due to the reliability requirement that at each instant the load and generation in the system remain balanced. In this study, we tackle this challenge for smart grid with integrated wind generation, by leveraging multi-timescale dispatch and scheduling. Specifically, we consider smart grids with two classes of energy users - traditional energy users and opportunistic energy users (e.g., smart meters or smart appliances), and investigate pricing and dispatch at two timescales, via day-ahead scheduling and realtime scheduling. In day-ahead scheduling, with the statistical information on wind generation and energy demands, we characterize the optimal procurement of the energy supply and the day-ahead retail price for the traditional energy users; in realtime scheduling, with the realization of wind generation and the load of traditional energy users, we optimize real-time prices to manage the opportunistic energy users so as to achieve systemwide reliability. More specifically, when the opportunistic users are non-persistent, i.e., a subset of them leave the power market when the real-time price is not acceptable, we obtain closedform solutions to the two-level scheduling problem. For the persistent case, we treat the scheduling problem as a multitimescale Markov decision process. We show that it can be recast, explicitly, as a classic Markov decision process with continuous state and action spaces, the solution to which can be found via standard techniques. We conclude that the proposed multi-scale dispatch and scheduling with real-time pricing can effectively address the volatility and uncertainty of wind generation and energy demand, and has the potential to improve the penetration of renewable energy into smart grids.Comment: Submitted to IEEE Infocom 2011. Contains 10 pages and 4 figures. Replaces the previous arXiv submission (dated Aug-23-2010) with the same titl

Overview of increasing the penetration of renewable energy sources in the distribution grid by developing control strategies and using ancillary services

Increasing the renewables energy resources in the distribution network is one of the main challenges of the distributed system operator due to instability, power quality and feeder capacity problems. This paper proposes a solution for further penetration of distributed energy resources, by developing control strategies and using ancillary services. Besides the penetration issues, the control strategies will mitigate power quality problems, voltage unbalance and will increase the immunity of the grid by provision of fault ride through capabilities

A Multiperiod OPF Model Under Renewable Generation Uncertainty and Demand Side Flexibility

Renewable energy sources such as wind and solar have received much attention in recent years and large amount of renewable generation is being integrated to the electricity networks. A fundamental challenge in power system operation is to handle the intermittent nature of the renewable generation. In this paper we present a stochastic programming approach to solve a multiperiod optimal power flow problem under renewable generation uncertainty. The proposed approach consists of two stages. In the first stage operating points for conventional power plants are determined. Second stage realizes the generation from renewable resources and optimally accommodates it by relying on demand-side flexibility. The benefits from its application are demonstrated and discussed on a 4-bus and a 39-bus systems. Numerical results show that with limited flexibility on the demand-side substantial benefits in terms of potential additional re-dispatch costs can be achieved. The scaling properties of the approach are finally analysed based on standard IEEE test cases upto 300 buses, allowing to underlined its computational efficiency.Comment: 8 pages, 10 figure

EcoBlock: Grid Impacts, Scaling, and Resilience

Widespread deployment of EcoBlocks has the potential to transform today's electricity system into one that is more resilient, flexible, efficient and sustainable. In this vision, the system will consist of self- su cient, renewable-powered, block-scale entities that can deliberately adjust their net power exchange and can optimize performance, maintain stability, support each other, or disconnect entirely from the grid as needed. This report is intended as an independent analysis of the potential relationships, both constructive and adverse, between EcoBlocks and the grid

Local flexibility market design for aggregators providing multiple flexibility services at distribution network level

This paper presents a general description of local flexibility markets as a market-based management mechanism for aggregators. The high penetration of distributed energy resources introduces new flexibility services like prosumer or community self-balancing, congestion management and time-of-use optimization. This work is focused on the flexibility framework to enable multiple participants to compete for selling or buying flexibility. In this framework, the aggregator acts as a local market operator and supervises flexibility transactions of the local energy community. Local market participation is voluntary. Potential flexibility stakeholders are the distribution system operator, the balance responsible party and end-users themselves. Flexibility is sold by means of loads, generators, storage units and electric vehicles. Finally, this paper presents needed interactions between all local market stakeholders, the corresponding inputs and outputs of local market operation algorithms from participants and a case study to highlight the application of the local flexibility market in three scenarios. The local market framework could postpone grid upgrades, reduce energy costs and increase distribution grids’ hosting capacity.Postprint (published version