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• Explicit mathematical model of interdependency between PMU cost and error analysis. • Formulation of a weighted goal programming framework applied to the OPP problem. • WGP allows for customization based on the user's needs and additional goals. • Improved topological observability constraint which considers all PMU measurements. This paper proposes a multi-objective mixed-integer linear programming (MILP) model for the optimal phasor measurement unit (PMU) placement (OPP) problem. A majority of the solutions presented for the OPP problem focus on minimizing cost while guaranteeing system observability. While this is a good approach to the OPP problem, the effect of PMU allocation on various energy management system applications should also be considered. This paper addresses the OPP problem considering PMU installation costs, system observability, and gross error detection. In order to allocate PMUs considering gross error detection, the Vulnerability Index (VI) is used to quantify the vulnerability of each element in the system. A weighted goal programming (WGP) framework, which allows for the optimization of contradictory goals, which is a characteristic of the goals considered in this paper, is presented. The goals and weights of the framework are user inputs to the program besides a hard budget restriction. The results of this paper show how the WGP framework allows the priorities of the decision maker to influence the final PMU allocation presented by the MILP, making this model a valuable framework for utilities planning to install PMUs on their network. [ABSTRACT FROM AUTHOR]

• We evaluate the impact of operational modeling details on resource adequacy results. • Multi-stage probabilistic assessments may better capture operational details. • Thermal generator outages may impact results more than solar forecast errors. • Flexibility provided by hybrids can reduce the number of load-shedding events. • Results are more sensitive to hybrid inverter sizing than other hybrid features. Assessing and maintaining resource adequacy (RA) is a core pillar of power systems. However, recent changes in the physical makeup of these systems and the conditions under which these systems must operate have yielded a renewed interest in the methods, metrics, and assumptions that underpin RA assessments. In this paper, we systematically explore a wide range of RA modeling dimensions, including: the objective function and level of operational detail in the underlying model formulation; the quantity (look-ahead) and quality (accuracy) of data that is available for making operational decisions within those models; and the physical configuration of solar photovoltaics (PV) with battery storage hybrid resources. We apply a set of probabilistic RA tools and production cost modeling tools to a realistic test system based loosely on a future Electric Reliability Council of Texas power system dominated by solar PV resources. Under the assumptions of our system and models, we find that multi-stage probabilistic assessments may provide a more robust evaluation of RA by capturing a wider range of operational and system interactions, but this comes at a computational cost of 1–2 orders of magnitude longer run time depending on the specific configuration. In addition, the information on thermal generator availability impacts RA performance by an order of magnitude more than solar resource forecasts, which is driven by the comparatively larger magnitude of thermal outages than solar forecast errors within our test system. Lastly, the flexibility provided by hybrid and other resources can help reduce system load-shedding event frequencies and enable the system to be more robust to inaccurate forecast information, and alternative hybrid inverter sizes can impact RA levels by 1–2 orders of magnitude. Our results point to the importance of a broader flexibility framework to describe the interaction between (1) flexibility "supply" from both physical resource capabilities and operational constraints considered in the modeling, and (2) flexibility "demand" from forecast errors, thermal generator outages, and other sources of uncertainty, as well as their RA impacts. Results are likely sensitive to the system buildout explored; future work could consider additional system configurations and conditions. [ABSTRACT FROM AUTHOR]