Risk Assessment of Industrial Energy Hubs and Peer-to-Peer Heat and Power Transaction in the Presence of Electric Vehicles

The peer-to-peer (P2P) strategy as a new trading scheme has recently gained attention in local electricity markets. This is a practical framework to enhance the flexibility and reliability of energy hubs, specifically for industrial prosumers dealing with high energy costs. In this paper, a Norwegian industrial site with multi-energy hubs (MEHs) is considered, in which they are equipped with various energy sources, namely wind turbines (WT), photovoltaic (PV) systems, combined heat and power (CHP) units (convex and non-convex types), plug-in electric vehicles (EVs), and load-shifting flexibility. The objective is to evaluate the importance of P2P energy transaction with on-site flexibility resources for the industrial site. Regarding the substantial peak power charge in the case of grid power usage, this study analyzes the effects of P2P energy transaction under uncertain parameters. The uncertainties of electricity price, heat and power demands, and renewable generations (WT and PV) are challenges for industrial MEHs. Thus, a stochastically based optimization approach called downside risk constraint (DRC) is applied for risk assessment under the risk-averse and risk-neutral modes. According to the results, applying the DRC approach increased by 35% the operation cost (risk-averse mode) to achieve a zero-based risk level. However, the conservative behavior of the decision maker secures the system from financial losses despite a growth in the operation cost

Peak-Load Management of Distribution Network Using Conservation Voltage Reduction and Dynamic Thermal Rating

The peak-load management of a distribution network (DN) has gained attention by increasing the electric power consumption on the demand side. By developing smart-grid infrastructures, effective utilization of the DN’s components and proper management of the DN would create a valuable solution for DN operators. Hence, in this paper, a peak-load management framework is proposed in which the real-time rating of the components and voltage-dependent features of the electric loads help the DN operator handle the peak times successfully. In addition to the individual advantages of efficient operation of the DN, more practical results are obtained by combining the conservation voltage reduction (CVR) and dynamic thermal rating (DTR) of the DN’s lines and transformers. Based on the obtained results, compared to the individual implementation of CVR, the cost-saving level is increased significantly during the peak events using the simultaneous utilization of DTR and CVR. Furthermore, a discussion is presented about the current problems of the feeders supplying the voltage-dependent constant-power loads during CVR utilization, which is resolved by the dynamic rating of the DN’s components

Continuous-Time Optimization of Integrated Networks of Electricity and District Heating Under Wind Power Uncertainty

The integrated operation of the electricity and district heating systems (EDHS) attracted lots of attention in recent years due to considerable impacts on the power system’s flexibility. The time intervals and mathematical methods used in the optimization procedure are essential, especially when flexible operation in the presence of intermittent renewable resources is an objective because of the sub-hourly dynamics. Due to the intrinsic deficiencies of the traditional discrete-time hourly models in handling the sub-hourly variation of the load and renewable generation, in this paper, a new continuous-time optimization model is proposed to model the look ahead operation of EDHS. The proposed continuous-time model is approximated by the linear spline-based trajectories and represented by the cubic splines of Bernstein function space to capture EDHS’s sub-hourly load and wind generation fluctuations. The EDHS of Barry Island is employed to investigate the proposed model and obtain results compared with the discrete-time procedure. Also, to measure the impact of uncertainties on both the continuous-time and discrete time models, the information gap decision theory (IGDT) is utilized. The examination results illustrate that the proposed continuous-time model brings a saving of 0.91% in the costs when compared with the discrete-time model on a small test system. In addition, the results of the IGDT technique show more opportunities by wind increasing and fewer threats by wind reduction using the proposed continuous-time optimization problem compared to the discrete-time model

Large-consumer energy procurement optimization using a hybrid IGDT-stochastic approach

The large electricity consumer (LEC) problem has been increasingly getting noticed from various viewpoints in recent years. Decreasing the total operation cost (TOC) of LEC with multi-energy procurement sources (MEPSs) is considered as a main objective for the decision-maker. So, to this end, in this paper, MEPSs contain pool market (PM), bilateral contracts (BCs), renewable energy sources (RESs), i.e., photovoltaic panels (PVs) and wind turbines (WTs), distributed generations (DGs), and also energy storage systems (ESSs). The flexible reducing expected cost of LEC, which is integrated into the presented model, is the demand response program (DRP). Also, to accommodate the uncertain nature of the output powers of RESs, demand, and electricity market price, a hybrid information-gap decision theory (IGDT)-stochastic approach is proposed in the current work. Finally, a case study is considered to apply the proposed mixed-integer linear programming (MILP) model and then investigate the presence of DRP in both risk-averse strategy (RAS) and risk-seeker strategy (RSS) for the LEC taken problem. Simulation results are obtained from CPLEX solver under GAMS optimization software indicate the potentiality and effectiveness of the introduced approach

Peak-Load Management of Distribution Network Using Conservation Voltage Reduction and Dynamic Thermal Rating

The peak-load management of a distribution network (DN) has gained attention by increasing the electric power consumption on the demand side. By developing smart-grid infrastructures, effective utilization of the DN’s components and proper management of the DN would create a valuable solution for DN operators. Hence, in this paper, a peak-load management framework is proposed in which the real-time rating of the components and voltage-dependent features of the electric loads help the DN operator handle the peak times successfully. In addition to the individual advantages of efficient operation of the DN, more practical results are obtained by combining the conservation voltage reduction (CVR) and dynamic thermal rating (DTR) of the DN’s lines and transformers. Based on the obtained results, compared to the individual implementation of CVR, the cost-saving level is increased significantly during the peak events using the simultaneous utilization of DTR and CVR. Furthermore, a discussion is presented about the current problems of the feeders supplying the voltage-dependent constant-power loads during CVR utilization, which is resolved by the dynamic rating of the DN’s components

Two-stage optimal risk management of large electricity consumer using second-order stochastic dominance

Various energy consumers, such as large energy consumers (LEC), are targeted to procure the demanded energy from various power markets such as the pool market and different energy resources, including renewable energy resources (RES), and conventional energy resources optimize the traded energy. In this article, a novel decision-making framework is proposed to schedule the LEC. The proposed technique in this article is based on the second-order stochastic dominance (SSD) to investigate the uncertainty in the total operation cost of the LEC. It is assumed that the market price, pool price, electricity load, and the power output of renewable energy sources (RES), including PV and WT, are uncertain parameters. In the proposed SSD-constrained stochastic programming, demand response programming (DRP) is provided to decrease the operation cost of the LEC. A case study is used to illustrate the effectiveness and efficiency of the novel SSD approach. According to the simulation results, the operation cost of LEC is remarkably decreased from 62,960 to 59,550 in the risk-neutral case (without including risk factor) and SSD case (worst case) with considering DRP, respectively