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In this paper, we present a novel open-source electricity systems optimization tool--the Holistic Optimization Program for Electricity (HOPE)--to assess emerging generation technology, inform policy design, and support planning. With a highly transparent, interpretable and compact model design, HOPE easily allows user access and modification, serving its main goal to benefit users beyond engineer communities and facilitate collaboration across the science-policy boundary. By activating different modes, the current version of HOPE (v1.0) offers flexibility in serving as either a Generation and Transmission Expansion Planning tool (GTEP) or a Production Cost Modelling tool (PCM). It includes modelling features such as long-term resource investments, short-term system operations, and a detailed representation of policies across various levels of regulated institutions. This paper outlines the building blocks of the model and its software structure. Case study results from using HOPE for the state of Maryland as well as Pennsylvania-New Jersey-Maryland (PJM) footprint are also provided. 

Transmission networks and generating units must be reinforced to satisfy the ever-increasing demand for electricity and to keep power system reliability within an acceptable level. According to the standards, the planned power system must be able to supply demand in the case of outage of a single element (N − 1 security criteria), and the possibility of cascading failures must be minimized. In this paper, we propose a risk-based dynamic generation and transmission expansion planning model with respect to the propagating effect of each contingency on the power system. Using the concept of risk, post-contingency load-shedding penalty costs are obtained and added in the objective function to penalize high-risk contingencies more dominantly. The McCormick relaxation is tailored to alter the objective function into a linear format. To keep the practicality of the proposed model, a second-order cone programming model is applied for power flow representation, and the problem is modeled in a dynamic time frame. The proposed model is formulated as a mixed-integer second-order cone programming problem. The numerical studies on the RTS 24-bus test system illustrate the efficacy of the proposed model.