Development of a smart grid for the proposed 33 KV ring main Distribution System in NIT Rourkela

The non-reliability of fossil fuels has forced the world to use energy efficiently. These days, it is being stressed to use the electrical power smartly so that energy does not go waste. And hence comes the concept of a Smart Grid. So it becomes necessary for reputed places of academics to develop the prototype of the same in their campus. National Institute of Technology (NIT) Rourkela intends to set up a 33KV Ring Main Distribution System including 33/0.433 KV substations in its campus. The present 11KV line will be discarded and replaced by the 33KV system. The main driving force behind this step by the management is to accommodate the stupendously increased power requirement of the institute. The above mentioned plan also includes, set up of Data Acquisition System (DAS) that intends to monitor the electrical equipment in the substations. This is being done not only to increase the accountability and reliability of the distribution system but also to encourage academic research in the distribution automation domain. All in all, an excellent step towards make the Grid, Smart. In this project work the focus is laid on getting load flow solution of the 33KV ring main system. Here the authors use a specialized algorithm for distribution network with high R/X value to obtain the load flow solution. Then using artificial neural networks computation, algorithms are implemented to do the load forecasting and dynamic tariff setting. At the end a Web Portal, the NITR e-Power Monitoring System is developed that will be an excellent interface to the public in general and will help the students of the institute to know their grid well. In short a conscious effort is put to make the grid more interactive

Stochastic Control and Pricing for Natural Gas Networks

We propose stochastic control policies to cope with uncertain and variable gas extractions in natural gas networks. Given historical gas extraction data, these policies are optimized to produce the real-time control inputs for nodal gas injections and for pressure regulation rates by compressors and valves. We describe the random network state as a function of control inputs, which enables a chance-constrained optimization of these policies for arbitrary network topologies. This optimization ensures the real-time gas flow feasibility and a minimal variation in the network state up to specified feasibility and variance criteria. Furthermore, the chance-constrained optimization provides the foundation of a stochastic pricing scheme for natural gas networks, which improves on a deterministic market settlement by offering the compensations to network assets for their contribution to uncertainty and variance control. We analyze the economic properties, including efficiency, revenue adequacy and cost recovery, of the proposed pricing scheme and make them conditioned on the network design.Comment: for associated GitHub repository, see https://github.com/anubhavratha/ng_stochastic_control_and_pricin

Moving from Linear to Conic Markets for Electricity

We propose a new forward electricity market framework that admits heterogeneous market participants with second-order cone strategy sets, who accurately express the nonlinearities in their costs and constraints through conic bids, and a network operator facing conic operational constraints. In contrast to the prevalent linear-programming-based electricity markets, we highlight how the inclusion of second-order cone constraints enables uncertainty-, asset- and network-awareness of the market, which is key to the successful transition towards an electricity system based on weather-dependent renewable energy sources. We analyze our general market-clearing proposal using conic duality theory to derive efficient spatially-differentiated prices for the multiple commodities, comprising of energy and flexibility services. Under the assumption of perfect competition, we prove the equivalence of the centrally-solved market-clearing optimization problem to a competitive spatial price equilibrium involving a set of rational and self-interested participants and a price setter. Finally, under common assumptions, we prove that moving towards conic markets does not incur the loss of desirable economic properties of markets, namely market efficiency, cost recovery and revenue adequacy. Our numerical studies focus on the specific use case of uncertainty-aware market design and demonstrate that the proposed conic market brings advantages over existing alternatives within the linear programming market framework.Comment: Manuscript with electronic companion; submitted to Operations Researc

Moving from Linear to Conic Markets for Electricity

International audienceWe propose a new forward electricity market framework that admits heterogeneous market participantswith second-order cone strategy sets, who accurately express the nonlinearities in their costsand constraints through conic bids, and a network operator facing conic operational constraints.In contrast to the prevalent linear-programming-based electricity markets, we highlight how theinclusion of second-order cone constraints improves uncertainty-, asset-, and network-awarenessof the market, which is key to the successful transition towards an electricity system based onweather-dependent renewable energy sources. We analyze our general market-clearing proposalusing conic duality theory to derive efficient spatially-differentiated prices for the multiple commodities,comprised of energy and flexibility services. Under the assumption of perfect competition,we prove the equivalence of the centrally-solved market-clearing optimization problem to a competitivespatial price equilibrium involving a set of rational and self-interested participants and a pricesetter. Finally, under common assumptions, we prove that moving towards conic markets does notincur the loss of desirable economic properties of markets, namely market efficiency, cost recovery,and revenue adequacy. Our numerical studies focus on the specific use case of uncertainty-awaremarket design and demonstrate that the proposed conic market brings advantages over existingalternatives within the linear programming market framework

Affine Policies for Flexibility Provision by Natural Gas Networks to Power Systems

Online appendix for the paper - "Affine Policies for Flexibility Provision by Natural Gas Networks to Power Systems", containing the power generators, gas producers and demand data as well as physical characteristics of the electrical transmission lines and gas pipelines. The empirically estimated forecast error covariance matrix used in the model is provided as well. The power systems and natural gas systems data is adapted from: C. Ordoudis, P. Pinson and J. M. González, "An integrated market for electricity and natural gas systems with stochastic power producers", European Journal of Operational Research, vol. 272, no. 2, pp. 642-654, 2019