Spatiotemporal Splitting of Distribution Networks into Self-Healing Resilient Microgrids using an Adjustable Interval Optimization

The distribution networks can convincingly break down into small-scale self-controllable areas, namely microgrids to substitute microgrids arrangements for effectively coping with any perturbations. To achieve these targets, this paper examines a novel spatiotemporal algorithm to split the existing network into a set of self-healing microgrids. The main intention in the grid-tied state is to maximize the microgrids profit while equilibrating load and generation at the islanded state by sectionalizing on-fault area, executing resources rescheduling, network reconfiguration and load shedding when the main grid is interrupted. The proposed problem is formulated as an exact computationally efficient mixed integer linear programming problem relying on the column & constraint generation framework and an adjustable interval optimization is envisaged to make the microgrids less susceptible against renewables variability. Finally, the effectiveness of the proposed model is adequately assured by performing a realistic case study.© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed

Multi-Interval Rolling-Window Joint Dispatch and Pricing of Energy and Reserve under Uncertainty

In this paper, the intra-day multi-interval rolling-window joint dispatch and pricing of energy and reserve is studied under increasing volatile and uncertain renewable generations. A look-ahead energy-reserve co-optimization model is proposed for the rolling-window dispatch, where possible contingencies and load/renewable forecast errors over the look-ahead window are modeled as several scenario trajectories, while generation, especially its ramp, is jointly scheduled with reserve to minimize the expected system cost considering these scenarios. Based on the proposed model, marginal prices of energy and reserve are derived, which incorporate shadow prices of generators' individual ramping capability limits to eliminate their possible ramping-induced opportunity costs or arbitrages. We prove that under mild conditions, the proposed market design provides dispatch-following incentives to generators without the need for out-of-the-market uplifts, and truthful-bidding incentives of price-taking generators can be guaranteed as well. Some discussions are also made on how to fit the proposed framework into current market practice. These findings are validated in numerical simulations

Current challenges for preseismic electromagnetic emissions: shedding light from micro-scale plastic flow, granular packings, phase transitions and self-affinity notion of fracture process

Are there credible electromagnetic (EM) EQ precursors? This a question debated in the scientific community and there may be legitimate reasons for the critical views. The negative view concerning the existence of EM precursors is enhanced by features that accompany their observation which are considered as paradox ones, namely, these signals: (i) are not observed at the time of EQs occurrence and during the aftershock period, (ii) are not accompanied by large precursory strain changes, (iii) are not accompanied by simultaneous geodetic or seismological precursors and (v) their traceability is considered problematic. In this work, the detected candidate EM precursors are studied through a shift in thinking towards the basic science findings relative to granular packings, micron-scale plastic flow, interface depinning, fracture size effects, concepts drawn from phase transitions, self-affine notion of fracture and faulting process, universal features of fracture surfaces, recent high quality laboratory studies, theoretical models and numerical simulations. Strict criteria are established for the definition of an emerged EM anomaly as a preseismic one, while, precursory EM features, which have been considered as paradoxes, are explained. A three-stage model for EQ generation by means of preseismic fracture-induced EM emissions is proposed. The claim that the observed EM precursors may permit a real-time and step-by-step monitoring of the EQ generation is tested

Analysis of market incentives on power system planning and operations in liberalised electricity markets

The design of liberalised electricity markets (e.g., the energy, capacity and ancillary service markets) is a topic of much debate, regarding their ability to trigger adequate investment in generation capacities and to incentivize flexible power system operation. Long-term generation investment (LTGI) models have been widely used as a decision-support tool for generation investments and design of energy policy. Of particular interest is quantification of uncertainty in model outputs (e.g., generation projections or system reliability) given a particular market design while accounting for uncertainties in input data as well as the discrepancies between the model and the reality. Unfortunately, the standard Monte Carlo based techniques for uncertainty quantification require a very large number of model runs which may be impractical to achieve for a complex LTGI model. In order to enable efficient and fully systematic analysis, it is therefore necessary to create an emulator of the full model, which may be evaluated quickly for any input and which quantifies uncertainty in the output of the full model at inputs where it has not been run. The case study shows results from the Great Britain power system exemplar which is representative of LTGI models used in real policy processes. In particular, it demonstrates the application of Bayesian emulation to a complex LTGI model that requires a formal calibration, uncertainty analysis, and sensitivity analysis. In power systems with large amounts of variable generation, it is important to provide sufficient incentives for operating reserves as a main source of generation flexibility. In the traditional unit commitment (UC) model, the demand for operating reserves is fixed and inelastic, which does not reflect the marginal value of operating reserves in avoiding the events of load shedding and wind curtailment. Besides, the system-wide reserve constraint assumes that the operating reserve can be delivered to any location freely, which is not true in real-world power system operations. To recognize the value and deliverability of operating reserves, dynamic zonal operating reserve demand curves are introduced to an enhanced deterministic UC model for co-optimizing the day-ahead schedules for energy and operating reserves. In the case study on the RTS-73 test system, comparisons are made between the choices of reserve policies (e.g., single, seasonal or dynamic zones) and of different reserve zonal partitioning methods. Results suggest that the enhanced deterministic UC model produces on average lower operational cost, higher system reliability and higher energy and reserve revenues than the traditional one. Finally, we discuss future directions of methodological research arising from current energy system challenges and the computer models developed for better understanding of the impacts of market incentives on power system planning and operations

Transmission Expansion Planning : computational challenges toward real-size networks

The importance of the transmission network for supplying electricity demand is undeniable, and Transmission Expansion Planning (TEP) studies is key for a reliable power system. Due to increasing sources of uncertainty such as more intermittent energy resources, mobile and controllable demands, and fast technology improvements for PVs and energy storage devices, the need for using systematic ways for solving this complex problem is increased. One of the main barriers for deploying optimization-based TEP studies is computationally intractability, which is the main motivation for this research. The aim of this work is to investigate the computational challenges associated with systematic TEP studies for large-scale problems, and develop algorithms to improve computational performance. In the first step, we investigate the impact of adding security constraints (as NERC standard requirement) into TEP optimization problem, and develop the Variable Contingency List (VCL) algorithm to pre-screen security constraints to only add those that may affect the feasible region. It significantly decreases the size of the problem compared to considering all security constraints. Then, we evaluate the impact of the size of candidate lines list (number of binary variables) on TEP, and developed a heuristic algorithm to decrease the size of this list. In the next step, we integrate uncertainties into the TEP optimization problem and formulate the problem as a two-stage stochastic program. Adding uncertainties increases the size of the problem significantly. It leads us to develop a three-level filter that introduces important scenario identification index (ISII) and similar scenario elimination (SSE) technique to decrease the number of security constraints in stochastic TEP in a systematic and tractable way. We then investigate the scalability of the stochastic TEP formulation. We develop a configurable decomposition framework that allows us to decompose the original problem into subproblems that can be solved independently and in parallel. This framework can benefit from using both progressive hedging (PH) and Benders decomposition (BD) algorithms to decompose and parallelize a large-scale problem both vertically and horizontally. We have also developed a bundling algorithm that improves the performance of PH algorithm and the overall performance of the framework. We have implemented our work on a reduced ERCOT network with more than 3000 buses to demonstrate the practicality of the proposed method in this work for large-scale problems.Electrical and Computer Engineerin

Virtus project: A scalable aggregation platform for the intelligent virtual management of distributed energy resources

open5noVIRTUS Project, funded by Cassa per i Servizi Energetici e Ambientali (Fund for Energy and Environmental Services-CSEA)-Project code CCSEB_00094.The VIRTUS project aims to create a Virtual Power Plant (VPP) prototype coordinating the Distributed Energy Resources (DERs) of the power system and providing services to the system operators and the various players of the electricity markets, with a particular focus on the industrial sector agents. The VPP will be able to manage a significant number of DERs and simulate realistic plants, components, and market data to study different operating conditions and the future impact of the policy changes of the Balancing Markets (BM). This paper describes the project’s aim, the general structure of the proposed framework, and its optimization and simulation modules. Then, we assess the scalability of the optimization module, designed to provide the maximum possible flexibility to the system operators, exploiting the simulation module of the VPP.openBianchi S.; De Filippo A.; Magnani S.; Mosaico G.; Silvestro F.Bianchi S.; De Filippo A.; Magnani S.; Mosaico G.; Silvestro F

Optimal provision of distributed reserves under dynamic energy service preferences

We propose and solve a stochastic dynamic programming (DP) problem addressing the optimal provision of regulation service reserves (RSR) by controlling dynamic demand preferences in smart buildings. A major contribution over past dynamic pricing work is that we pioneer the relaxation of static, uniformly distributed utility of demand. In this paper we model explicitly the dynamics of energy service preferences leading to a non-uniform and time varying probability distribution of demand utility. More explicitly, we model active and idle duty cycle appliances in a smart building as a closed queuing system with price-controlled arrival rates into the active appliance queue. Focusing on cooling appliances, we model the utility associated with the transition from idle to active as a non-uniform time varying function. We (i) derive an analytic characterization of the optimal policy and the differential cost function, and (ii) prove optimal policy monotonicity and value function convexity. These properties enable us to propose and implement a smart assisted value iteration (AVI) algorithm and an approximate DP (ADP) that exploits related functional approximations. Numerical results demonstrate the validity of the solution techniques and the computational advantage of the proposed ADP on realistic, large-state-space problems