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Worldwide, environmental regulations such as Renewable Portfolio Standards (RPSs) are being broadly adopted to promote renewable energy investments.With corresponding increases in renewable energy deployments, there is growing interest in grid-scale energy storage systems (ESS) to provide the flexibility needed to efficiently deliver renewable power to consumers. Our contribution in this paper is to introduce a unified generation, transmission, and bulk ESS expansion planning model subject to an RPS constraint, formulated as a two-stage stochastic mixed-integer linear program (MILP) optimization model, which we then use to study the impact of co-optimization and evaluate the economic interaction between investments in these three classes of assets in achieving high renewables penetrations. We present numerical case studies using the 24-bus IEEE RTS-96 test system considering wind and solar as available renewable energy resources, and demonstrate that up to $180 million/yr in total cost savings can result from the co-optimization of all three assets, relative to a situation in which no ESS investment options are available. Surprisingly, we find that co-optimized bulk ESS investments provide significant economic value through investment deferrals in transmission and generation capacity, but very little cost savings in operational cost. Finally, we observe that planning transmission and generation infrastructure first and later optimizing ESS investments-as is common in industry-captures at most 1.7% ($3 million/yr) of the savings that result from co-optimizing all assets simultaneously.Keywords: Energy storage, Renewable portfolio standards, Transmission expansion planning 2010 MSC: 00-01, 99-00 45 however, it is clear that operational changes alone cannot achieve the balancing needed at very high RPS levels and that investment into new network infrastructure will need to be considered.While pumped-hydro storage remains one of the lowest cost bulk ESS technologies in terms of MWh stored, faster-responding technologies such as fly-50 wheels, super capacitors, and batteries (e.g., lithium-ion, vanadium redox flow, zinc-air, and sodium-sulfur) can provide power and ancillary services that reinforce grid stability [13]. Third party investment has also been boosted by Federal 3 Energy Regulatory Commission Order No. 755 and subsequent Order No. 784, which require creation of a market for "fast and accurate" frequency regulation 55 services for third party merchants, which is favorable for fast-responding ESS technologies [14, 15]. In this paper, however, we focus specifically on bulk energy storage services, which include load shifting, peak shaving, and generation and transmission investment deferrals [16].Currently, the PJM Interconnection (US) has invested in over 100 MW of 60 battery energy storage for frequency regulation, while the Sistema Interconectado del Norte Grande (Chile) has installed over 30 MW for the similar use [17]. Further, the California Public Utilities Commission has mandated that the state's thre...

There exist several different reliability-and approximation-based methods to determine the contribution of solar resources towards resource adequacy. However, most of these approaches require knowing in advance the installed capacities of both conventional and solar generators. This is a complication since generator capacities are actually decision variables in capacity planning studies. In this article we study the effect of time resolution and solar PV penetration using a planning model that accounts for the full distribution of generator outages and solar resource variability. We also describe a modification of a standard deterministic planning model that enforces a resource adequacy target through a reserve margin constraint. Our numerical experiments show that at least 50 days worth of data are necessary to approximate the results of the full-resolution model with a maximum error of 2.5% on costs and capacity. We also show that the amount of displaced capacity of conventional generation decreases rapidly as the penetration of solar PV increases. We find that using an exogenously defined and constant capacity value based on time-series data can yield relatively accurate results for small penetration levels. For higher penetration levels, the modified deterministic planning model better captures avoided costs and the decreasing value of solar PV.