Stochastic Security Constrained Unit Commitment with High Penetration of Wind Farms

Secure and reliable operation is one of the main challenges in restructured power systems. Wind energy has been gaining increasing global attention as a clean and economic energy source, despite the operational challenges its intermittency brings. In this study, we present a formulation for electricity and reserve market clearance in the presence of wind farms. Uncertainties associated with generation and line outages are modeled as different system scenarios. The formulation incorporates the cost of different scenarios in a two-stage short-term (24-hours) clearing process, also considering different types of reserve. The model is then linearized in order to be compatible with standard mixed-integer linear programming solvers, aiming at solving the security constrained unit-commitment problem using as few variables and optimization constraints as possible. As shown, this will expedite the solution of the optimization problem. The model is validated by testing it on a case study based on the IEEE RTS1, for which results are presented and discussed.© 2019 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

Real-Time Control of Power Exchange at Primary Substations: An OPF-Based Solution

Nowadays, integration of more renewable energy resources into distribution systems to inject more clean en- ergy introduces new challenges to power system planning and operation. The intermittent behaviour of variable renewbale resources such as wind and PV generation would make the energy balancing more difficult, as current forecasting tools and existing storage units are insufficient. Transmission system operators may withstand some level of power imbalance, but fluctuations and noise of profiles are undesired. This requires local management performed or encouraged by distribution system operators. They could try to involve aggregators to exploit flexibility of loads through demand response schemes. In this paper, we present an optimal power flow-based algorithm written in Python which reads flexibility of different loads offered by the aggregators from one side, and the power flow deviation with respect to the scheduled profile at transmission-distribution coupling point from the other side, to define where and how much load to adjust. To demonstrate the applicability of this core, we set-up a real- time simulation-based test bed and realised the performance of this approach in a real-like environment using real data of a network

Network-constrained joint energy and flexible ramping reserve market clearing of power- and heat-based energy systems : a two-stage hybrid IGDT-stochastic framework

This article proposes a new two-stage hybrid stochastic–information gap-decision theory (IGDT) based on the network-constrained unit commitment framework. The model is applied for the market clearing of joint energy and flexible ramping reserve in integrated heat- and power-based energy systems. The uncertainties of load demands and wind power generation are studied using the Monte Carlo simulation method and IGDT, respectively. The proposed model considers both risk-averse and risk-seeker strategies, which enables the independent system operator to provide flexible decisions in meeting system uncertainties in real-time dispatch. Moreover, the effect of feasible operating regions of the combined heat and power (CHP) plants on energy and flexible ramping reserve market and operation cost of the system is investigated. The proposed model is implemented on a test system to verify the effectiveness of the introduced two-stage hybrid framework. The analysis of the obtained results demonstrates that the variation of heat demand is effective on power and flexible ramping reserve supplied by CHP units.©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

A conservative framework for obtaining uncertain bands of multiple wind farms in electric power networks by proposed IGDT-based approach considering decision-maker's preferences

Exploiting clean energy resources (CERs) is an applicable way to enhance sustainable development and have the cleaner production of electricity. On the other hand, variability and intermittency of these clean resources are the important disadvantages for determining the reliable operation of electrical grids. Thus, using the uncertainty modeling techniques seems necessary to have more practical values for the decision-making variables. The current paper demonstrates a novel architecture based on Information Gap Decision Theory (IGDT) to model the randomness of multiple Wind Farms (WFs) existing in electric power networks. Note that employing only the IGDT technique cannot consider the preferences defined by the decision-maker. In contrast, the proposed method tackles this issue by considering different values for radii of uncertainty related to the uncertain parameters. It has been proven that the presented approach is time-saving if compared with Monte Carlo Simulation (MCS) and the Epsilon-constraint-based-IGDT. Moreover, the execution time of the presented methodology does not considerably depend on the number of WFs for a power system. It means that if the number of WFs increases in a particular case study, consequently, the execution time does not noticeably rise if compared with the MCS and the Epsilon-constraint-based-IGDT. Furthermore, the equivalent Mixed Integer Linear Programming (MILP) of the original model is employed to guarantee the optimum solution. The performances of the presented methodology have been demonstrated by utilizing IEEE 30 BUS and IEEE 62 BUS systems.© 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Two-stage Robust-Stochastic Electricity Market Clearing Considering Mobile Energy Storage in Rail transportation

This paper proposes a two-stage robust-stochastic framework to evaluate the effect of the battery-based energy storage transport (BEST) system in a day-ahead market-clearing model. The model integrates the energy market-clearing process with a train routing problem, where a time-space network is used to describe the limitations of the rail transport network (RTN). Likewise, a price-sensitive shiftable (PSS) demand bidding approach is applied to increase the flexibility of the power grid operation and reduce carbon emissions in the system. The main objective of the proposed model is to determine the optimal hourly location, charge/discharge scheduling of the BEST system, power dispatch of thermal units, flexible loads scheduling as well as finding the locational marginal price (LMP) considering the daily carbon emission limit of thermal units. The proposed two-stage framework allows the market operator to differentiate between the risk level of all existing uncertainties and achieve a more flexible decision-making model. The operator can modify the conservatism degree of the market-clearing using a non-probabilistic method based on info-gap decision theory (IGDT), to reduce the effect of wind power fluctuations in real-time. In contrast, a risk-neutral-based stochastic technique is used to meet power demand uncertainty. The results of the proposed mixed-integer linear programming (MILP) problem, confirm the potential of BEST and PSS demand in decreasing the LMP, line congestion, carbon emission, and daily operation cost

Stochastic optimization model for coordinated operation of natural gas and electricity networks

Renewable energy sources will anticipate significantly in the future energy system paradigm due to their low cost of operation and low pollution. Considering the renewable generation (e.g., wind) intermittency, flexible gas-fired power plants will continue to play their essential role as the main linkage of natural gas and electricity networks, and hence coordinated operation of these networks is beneficial. Furthermore, uncertainty is always found in gas demand prediction, electricity demand prediction, and output power of wind generation. Therefore, in this paper, a two-stage stochastic model for operation of natural gas and electricity networks is implemented. In order to model uncertainty in these networks, Monte Carlo simulation is applied to generate scenarios representing the uncertain parameters. Afterwards, a scenario reduction algorithm based on distances between the scenarios is applied. Stochastic and deterministic models for natural gas and electricity networks are optimized and compared considering integrated and iterative operation strategies. Furthermore, the value of flexibility options (i.e., electricity storage systems) in dealing with uncertainty is quantified. A case study is presented based on a high pressure 15-node gas system and the IEEE 24-bus reliability test system to validate the applicability of the proposed approach. The results demonstrate that applying the stochastic model of gas and electricity networks as well as considering integrated operation strategy in the presence of flexibility provides different benefits (e.g., 14% cost savings) and enhances the system reliability in the case of contingency

Day-ahead optimal battery operation in islanded hybrid energy systems and its impact on greenhouse gas emissions

This paper proposes a management strategy for the daily operation of an isolated hybrid energy system (HES) using heuristic techniques. Incorporation of heuristic techniques to the optimal scheduling in day-head basis allows us to consider the complex characteristics of a specific battery energy storage system (BESS) and the associated electronic converter efficiency. The proposed approach can determine the discharging time to perform the load peak-shaving in an appropriate manner. A recently proposed version of binary particle swarm optimization (BPSO), which incorporates a time-varying mirrored S-shaped (TVMS) transfer function, is proposed for day-ahead scheduling determination. Day-ahead operation and greenhouse gas (GHG) emissions are studied through different operating conditions. The complexity of the optimization problem depends on the available wind resource and its relationship with load profile. In this regard, TVMS-BPSO has important capabilities for global exploration and local exploitation, which makes it a powerful technique able to provide a high-quality solution comparable to that obtained from a genetic algorithm

Economic operational analytics for energy storage placement at different grid locations and contingency scenarios with stochastic wind profiles

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.The placement of energy storage systems (ESS) in smart grids is challenging due to the high complexity of the underlying model and operational datasets. In this paper, non-parametric multivariate statistical analyses of the energy storage operations in base and contingency scenarios are carried out to address these issues. Monte Carlo simulations of the optimization process for the overall cost involving unit commitment and dispatch decisions are performed with different wind and load demand ensembles. The optimization is performed for different grid contingency scenarios like transmission line trips and generator outages along with the location of the ESS in different parts of the grid. The stochastic mixed-integer programming technique is used for optimization. The stochastic model load demand and wind power are obtained from real data. The uncertainty in the operational decisions is obtained, considering the different stochastic realizations of load demand and wind power. The data analytics is performed on ESS operations in the base and its corresponding contingency scenarios with different locations in the grid. Moreover, it is aided by non-parametric multivariate hypothesis tests to understand their dependence amongst various parameters and locations in the grid. The numerical analysis has been shown on a simple 3-bus system considering all the locational and contingency scenarios.F ERDF Cornwall New Energy (CNE