Optimal Demand Response Program for Flexible Ramps Markets

The augmented renewable penetration due to the growth of environmental concerns increases the necessity of additional flexibility because of supply variability, and it reduces the existing flexibility level by displacing with conventional units due to the priority in dispatch for the renewable resources. Therefore, conventional units have to start-up, shut-down and ramp up/down more frequently to preserve the system balance in real-time which may result in common damage mechanisms such as thermal shock, metal fatigue, corrosion, erosion and heat decay [1]. To overcome this issue, it has been proposed to add the ramping costs into the generation scheduling procedure with the aim of compensating the economic and technical losses of generation units [2]-[4]. The inclusion of ramping costs in the day-ahead scheduling has been studied in [2].The effects of the variable nature of renewable generations on ramping costs of thermal units have been evaluated in [3]. The ramping costs have been incorporated into the generation scheduling problem in the presence of uncertain renewable generations [4]. However, the mentioned works have not modeled the ramp market. Practically, in order to compensate a partial loss of conventional generators and incentivize them to provide both upward and downward flexible ramp, a well-functioning market has been developed so-called "flexiramp" in California ISO (CAISO) [5] and "ramp capability" in Midcontinent ISO (MISO) [6] along with energy and reserve markets in order to ensure the rampability of reserve capacity provided by its generation mixture to cope with sudden net load variations.The creation of flexible ramp products in real-time ISO markets has been investigated in [7] with a set of simplified assumptions such as ignoring the transmission constraints and supposing predefined day-ahead decisions. Formulations for the day-ahead energy and flexible ramp markets' clearing have been proposed in [8] and [9], respectively. The authors in [9] discussed the role of electric vehicles participation in the ramp market, whereas [9] dealt with the evaluation of the impacts of natural gas delivery system modeling and demand response on flexible ramp deployment. Despite the mentioned reports in the literature, finding the optimal demand response programs in the flexible ramp markets has not been addressed

DISTRIBUTION NETWORK OPERATION WITH SOLAR PHOTOVOLTAIC AND ENERGY STORAGE TECHNOLOGY

Among distributed energy resources, solar photovoltaic (PV) generation has the largest penetration in the distribution networks. Serving electric vehicles (EV) with renewable resource generation would further reduce the carbon footprint of the energy supply chain for electric vehicles. However, the integration of solar PV and EVs in the unbalanced distribution network introduces several challenges including voltage fluctuations, voltage imbalances, reverse power flow, and protection devices’ malfunctions. The uncertainties associated with solar PV integration and electric vehicles operation require significant effort to develop accurate optimization methodologies in the unbalanced distribution systems operation. In this thesis, in order to cope with the uncertainties, we first developed a two-stage optimization problem, to identify the feasible dispatch margins of photovoltaic generation considering the distribution network operation constraints. The dispatch margins of photovoltaic generation are quantified considering the worst-case realization of demand in the distribution network. The linear and the second-order cone mathematical problem formulation is procured to solve the optimal power flow problem. Second, a data-driven distributionally robust optimization framework is proposed for the operation of the unbalanced distribution network considering the uncertainties associated with the interconnected EV fleets and solar PV generation, and the proposed framework leverages the column-and-constraint generation approach. Moreover, to minimize the operation cost and improve the ramping flexibility, a continuous-time optimization problem, is developed and reformulated to a linear programming problem using Bernstein polynomials. Here, a generalized exact linear reformulation of the data-driven distributionally robust optimization is used to capture the worst-case probability distribution of the net demand uncertainties. Furthermore, in this thesis, an interconnection of multi microgrids (MGs) technology is considered a promising solution to handle the variability of the distributed renewable energy resources and improve the energy resilience in the distribution network. The coordination among the microgrids in the distribution network could improve the operation cost, reliability, and security of the distribution network. Therefore, an adaptive robust distributed optimization framework is developed for the operation of a distribution network with interconnected microgrids considering the uncertainties in demand and solar PV generation

Journal of the House of Representatives of the 88th GA 2, 2020

The published daily journals of the transactions of the House of Representatives for the current legislative session and the official bound journals printed after adjournment for previous legislative sessions