Hybrid IGDT-stochastic self-scheduling of a distributed energy resources aggregator in a multi-energy system

The optimal management of distributed energy resources (DERs) and renewable-based generation in multi-energy systems (MESs) is crucial as it is expected that these entities will be the backbone of future energy systems. To optimally manage these numerous and diverse entities, an aggregator is required. This paper proposes the self-scheduling of a DER aggregator through a hybrid Info-gap Decision Theory (IGDT)-stochastic approach in an MES. In this approach, there are several renewable energy resources such as wind and photovoltaic (PV) units as well as multiple DERs, including combined heat and power (CHP) units, and auxiliary boilers (ABs). The approach also considers an EV parking lot and thermal energy storage systems (TESs). Moreover, two demand response (DR) programs from both price-based and incentive-based categories are employed in the microgrid to provide flexibility for the participants. The uncertainty in the generation is addressed through stochastic programming. At the same time, the uncertainty posed by the energy market prices is managed through the application of the IGDT method. A major goal of this model is to choose the risk measure based on the nature and characteristics of the uncertain parameters in the MES. Additionally, the behavior of the risk-averse and risk-seeking decision-makers is also studied. In the first stage, the sole-stochastic results are presented and then, the hybrid stochastic-IGDT results for both risk-averse and risk-seeker decision-makers are discussed. The proposed problem is simulated on the modified IEEE 15-bus system to demonstrate the effectiveness and usefulness of the technique.© 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

Day-Ahead Scheduling for Economic Dispatch of Combined Heat and Power with Uncertain Demand Response

This paper presents an energy management method for the interconnected operation of power, heat, Combined Heat and Power (CHP) units to settle the Day-Ahead market in the presence of a demand response program (DRP). A major challenge in this regard is the price uncertainty for DRP participants. First, the definitive model of the problem is introduced from the perspective of the Regional Market Manager (RMM) in order to minimize the total supply cost in the presence of TOU program, which is a type of DRP. Furthermore, a market-oriented tensile model is presented in the form of a combination of over-lapping generations (OLG) and price elasticity (PE) formulations to determine the amount of electricity demand in the TOU program. Then, a price uncertainty model of the proposed problem is introduced according to the IGDT risk aversion and risk-taking strategies considering information gap decision theory (IGDT). The above problem is solved through the use of the co-evolutionary particle swarm optimization (C-PSO) algorithm and the proposed model is implemented on a standard seven-unit system for a period of 24 hours.© 2022 authors. Published by IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/fi=vertaisarvioitu|en=peerReviewed

Managing power system congestion and residential demand response considering uncertainty

Electric power grids are becoming increasingly stressed due to political and environmental difficulties in upgrading transmission capacity. This challenge receives even more interest with the paradigm change of increasing renewable energy sources and demand response (DR) programs. Among DR technologies, existing DR programs are primarily designed for industrial and commercial customers. However, household energy consumption accounts for 38% of total electricity consumption in the U.S., suggesting a significant missed opportunity. This dissertation presents an in-depth study to investigate managing power system congestion and residential DR program under uncertainty.First, an interval optimization model is presented for available transfer capability (ATC) evaluation under uncertainties. The conventional approaches of ATC assessment include deterministic and probabilistic methods. However, the proposed interval optimization model can effectively reduce the accuracy requirements on the renewable forecasting, and lead to acceptable interval results by mitigating the impacts of wind forecasting and modeling errors. Second, a distributed and scalable residential DR program is proposed for reducing the peak load at the utility level. The proposed control approach has the following features: 1) it has a distributed control scheme with limited data exchange among agents to ensure scalability and data privacy, and 2) it reduces the utility peak load and customers’ electricity bills while considering household temperature dynamics and network flow.Third, the impacts of weather and customers’ behavior uncertainties on residential DR are also studied in this dissertation. A new stochastic programming-alternating direction method of multipliers (SP-ADMM) algorithm is proposed to solve problems related to weather and uncertain customer behavior. The case study suggests that the performance of residential DR programs can be further improved by considering these stochastic parameters.Finally, a deep deterministic policy gradient-based (DDPG-based) HVAC control strategy is presented for residential DR programs. Simulation results demonstrate that the DDPG-based approach can considerably reduce system peak load, and it requires much less input information than the model-based methods. Also, it only takes each agent less than 3 seconds to make HVAC control actions. Therefore, the proposed approach is applicable to online controls or the cases where accurate building models or weather forecast information are not available

Optimal operation of an energy hub considering the uncertainty associated with the power consumption of plug-in hybrid electric vehicles using information gap decision theory

© 2019 Elsevier Ltd An energy hub is a multi-carrier energy system that is capable of coupling various energy networks. It increases the flexibility of energy management and creates opportunities to increase the efficiency and reliability of energy systems. When plug-in hybrid electric vehicles (PHEVs)are incorporated into the energy hub, batteries can act as an aggregated storage system, increasing the potential integration of variable renewable energy sources (RES)into power system networks. This paper presents a new model for the optimal operation of an energy hub that includes RES, PHEVs, fuel cell vehicles, a fuel cell, an electrolyzer, a hydrogen tank, a boiler, an inverter, a rectifier, and a heat storage system. A novel model is developed to estimate the uncertainty associated with the power consumption of PHEVs during trips using information gap decision theory (IGDT)under risk-averse and risk-seeking strategies. Simulation results demonstrate that the proposed method maximizes the objective function under the risk-neutral and risk-averse strategies, while minimizing the objective function under the risk-seeking strategy. Results from the modeling show that considering the uncertainty associated with the power consumption of PHEVs using IGDT enables the energy hub operator to make appropriate decisions when optimizing the operation of the energy hub against possible changes in power consumption of PHEVs

A novel hybrid two-stage framework for flexible bidding strategy of reconfigurable micro-grid in day-ahead and real-time markets

Microgrids are going to be used in future intelligent grids as a promising technology to enable widespread utilization of renewable energy sources in a highly efficient and reliable manner. It is known that reconfiguration of micro-grids, using tie-line and sectionalizing switches, can provide more operational flexibility. Additionally, coordinated scheduling of flexible loads and energy storage systems can play an important role in the optimal scheduling of micro-grids; thus lowering the costs. This paper proposes an optimal bidding strategy for a micro-grid in day-ahead and real-time markets, based on AC power flow model, considering the hourly reconfiguration of the micro-grid. Fuel cell-based hydrogen energy storage and multiple shiftable loads are considered in the proposed method according to the load’s activity schedule. A reconfigurable micro-grid incorporates energy production and consumption of its local components to trade power in both day-ahead and real-time markets in order to maximize its profit as a private entity. The bidding problem faces issues due to the high level of uncertainties, consisting of wind power generation and electric load as well as variations of market prices. A hybrid two-stage bi-level optimization model is proposed to manage such uncertainties so that wind power, load demand, and day-ahead market prices are handled through scenario-based stochastic programming, and an information gap decision theory is applied to model the uncertainty of real-time market prices under two strategies, namely risk-seeker and risk-averse. The numerical simulation results confirm the effectiveness of the proposed model