This paper proposes a methodology to solve generation expansion equilibrium problems by using a predictive model to represent the equilibrium in a simplified network constrained electricity market. The investment problem for each generation company (Genco) is a bi-level problem with the investment decision made in the upper level and market clearing condition in the lower level, which traditionally is represented as a Mathematical Program with Equilibrium Constraint (MPEC). The predictive model is trained for estimating the system-wide revenues for each technology type across energy, ancillary services and capacity markets given the amount of technology-specific installed capacity on the grid. The profit maximization investment problem for each Genco is solved using a global search algorithm, which uses the predictive model to evaluate the objective function. To solve for the strategic equilibrium, each Genco's problem is plugged into a diagonalization algorithm that is generally used in multi-leader, single-follower bi-level problems. The methodology presented here enables significant computational improvements while still capturing the desired market characteristics and dynamics of traditional equilibrium modeling approaches

Wholesale electricity markets are intended to incentivize system generation investments and operations outcomes that meet evolving system needs. In this work, we evaluate the effectiveness of wholesale market structures, rules and policies in achieving system resource adequacy (RA) and clean energy targets in the presence of self-interested generation investors using the Electricity Markets and Investment Suite Agent-based Simulation (EMIS-AS) model. Results highlight that both capacity markets and operating reserve demand curves (ORDCs) can help achieve a reliable system but with different RA compliance timelines and distribution of generation technologies. Structures with capacity markets tend to favor more capital-intensive peaking technologies while reducing wind and solar build-outs due to suppressed energy and clean energy market prices, particularly in the absence of strong clean energy targets. Conversely, ORDCs improve the commitment of available generation units, but this comes at the expense of higher system costs and renewable generation curtailment. We also find that well-calibrated static capacity demand curves can yield similar reliability and total cost compared to capacity market demand curves informed dynamically by resource adequacy while also yielding stable annual capacity prices. Different approaches to formulating ORDC curves can also yield key trade-offs, namely that a more efficient treatment of storage chronology results in lower ORDC curves and prices, yielding less investment and cost but at the expense of reliability. Finally, the effectiveness of wholesale electricity markets in practically achieving very high clean energy generation targets highly depends on the cost-competitiveness of clean energy technologies that can support critical balancing needs across multiple timescales.

The ongoing transformation of power systems and changing environment, including increasing amounts of storage and more frequent extreme weather events, has created challenges and necessitated changes in assessing and ensuring resource adequacy in competitive wholesale electricity markets. In this paper, we analyze several capacity remuneration mechanisms (namely operating reserve demand curves, or ORDCs, and capacity markets) in a system like the Electric Reliability Council of Texas (ERCOT) system, and we explore their impacts on profit-seeking firms’ investment decisions and resource adequacy implications under different weather conditions using the Electricity Markets Investment Suite Agent-based Simulation (EMIS-AS) model. Our results show that electricity markets without either ORDCs or capacity markets are unlikely to attract sufficient investments to maintain targeted resource adequacy levels. In addition, capacity markets and ORDC differ in how they support resource adequacy: capacity markets incentivize more overall capacity than ORDC alone, while ORDC better signals for commitment of capacity in the day-ahead. Weather patterns may also affect firms’ investment decisions. In particular, higher net load volatilities may contribute to more battery investments because of the potential of more arbitrage opportunities. We also show that even though all ORDCs and capacity markets achieve similar resource adequacy results, total system costs and the associated market revenues can be different. This result suggests total system costs and market revenues under different market designs are less robust to weather patterns, which highlights the need to evaluate market designs across a variety of weather years.

Competitive wholesale electricity markets can help facilitate energy system decarbonization by incentivizing investments in clean energy technologies that meet evolving system needs. We explore market structure impacts on generator operations and deployment by risk-averse, heterogeneous investor firms using the Electricity Markets and Investment Suite - Agent-based Simulation (EMIS-AS) model. We apply clean energy targets of 45%-100% by 2035 considering energy, ancillary services, capacity, and clean energy credit products and pricing and eligibility rules. Results highlight a complexity conundrum, whereby finding the "right" market design to achieve decarbonization goals and avoid unintended consequences can be a highly-nuanced, non -in-cremental challenge. Carefully designed energy-only markets can achieve the same clean energy targets as ca-pacity market structures but with different revenue and profitability outcomes. Operating reserve demand curve -based scarcity pricing can substitute capacity markets for similar deployment outcomes. Carbon pricing alone is most effective at achieving decarbonization levels at low clean energy targets, and clean energy credit markets and carbon pricing are substitutionary at high clean energy targets. Restricting technology participation in ca-pacity and operating reserve markets can impact deployment and operations, even for nonrestricted technolo-gies. Adding an inertia product with fast frequency response yields insufficient provision at high clean energy targets, but work is needed to understand frequency requirements and capabilities.

Investment decisions in the electricity sector are complex and depend on wholesale market and policy structures, attributes of investor firms that impact risk and financing, and the location-specific economics of investment options. This paper introduces the Electricity Markets and Investment Suite - Agent-Based Simulation (EMIS-AS), which models the evolution of the electricity generation mix under various market structures while explicitly capturing the aforementioned investment factors and imperfect information. EMIS-AS advances the state-of-the-art of generation expansion planning and agent-based modeling by incorporating various aspects of investor heterogeneity (e.g., differences in financial characteristics, technology preferences, and attitudes towards risk under uncertainty), a robust price prediction methodology, a methodology for updating investors’ forecast parameters using Kalman Filters, and endogenous representation of a customizable set of wholesale electricity markets including energy, ancillary services, capacity, and renewable energy certificate markets. Implementation of EMIS-AS on a test system highlights the strong role that firms’ heterogeneous attributes have on the investment decisions, generation portfolio, and resulting resource adequacy. In multiple instances, investment and retirement results diverge not only due to each firm’s own parameters, but also due to the actions and characteristics of other firms. Results also demonstrate how imperfect information and risk preferences can lead to suboptimal investment outcomes, which can require firm-level recourse actions with severe profitability implications. In addition, a comparison with a traditional generation expansion planning model highlights the ability of EMIS-AS to capture resource scarcity and early retirements caused by real-world imperfections that traditional models cannot represent.

Marine energy, including ocean waves, ocean currents, ocean thermal gradients, tides, and river currents, is a vast and untapped resource that can be harnessed to help enable the transition to renewable energy. Marine energy is an attractive renewable resource because of its energy density, predictability, and persistence. Further, marine energy has the potential to provide energy for utility-scale applications, remote and distributed applications, and rapidly expanding maritime industries, such as aquaculture and shipping. Marine energy technologies are, however, at a nascent stage of development, and a significant amount of the resource is located far from population centers and transmission infrastructure. Accordingly, to unlock the full potential of marine energy, efficient methods of storing and transporting captured marine energy are needed so that the energy can be used when and where it is needed. A promising solution to these energy storage and transportation challenges is to combine marine energy and hydrogen generation technologies. Herein, we provide a high-level analysis of the unique value proposition and technical challenges of combining marine energy and hydrogen technologies. First, we review marine energy technologies, electrolysis technologies, and hydrogen storage methods. Next, we consider specific applications and opportunities for combining the two technologies. Finally, we identify critical R&D challenges that must be overcome to successfully combine marine energy and hydrogen generation technologies. As part of our fact-finding effort in this area, we held a workshop attended by marine energy and hydrogen technology experts from industry, academia, national labs, and government entities to explore the technical challenges and opportunities for combined marine energy and hydrogen generation systems. Our intent is that this document and the report from the workshop can be used in conjunction to help identify and direct research and development that is needed to realize the potential of marine energy-hydrogen systems.

Electric vehicle managed charging can benefit all consumers by supporting grid planning, operation, and reliability – especially complementing high-renewable systems.

The value proposition of residential thermal demand response (DR) could be enhanced through participation in multiple electricity markets. However, this potential value could be affected by misalignment of the strategic objectives of DR participants and also by the adoption levels of flexible heating technologies. This article presents an extended multi-perspective model (EMPM) which integrates the optimization problems of the DR participants within a single framework to evaluate the participation of residential thermal DR in energy and capacity markets. The capacity market participation is explicitly modeled based on parameters obtained using a bespoke capacity value assessment methodology. Subsequently, an iterative algorithm is proposed for determining consumer adoption levels of flexible heating technologies. The results indicate that while capacity market participation of residential thermal DR would be beneficial for all DR entities, consumers’ comfort preferences would have a noticeable impact on the results. Additionally, very high consumer adoption levels of flexible technologies would reduce power system costs and increase investment in low-carbon generation, but would not be optimal from the consumers’ investment perspective. Additionally, sensitivity analyses highlight that multiple market participation, energy inefficiency of the building fabric and greater preference for comfort could incentivize greater consumer adoption of flexible heating technologies.

Multicarrier energy systems (MCESs) are characterized by strong coordination in operation and planning across multiple energy vectors and/or sectors to deliver reliable, cost-effective energy services to end users/customers with minimal impact on the environment. They have efficiency and flexibility benefits and are deployed in large and small scales on the supply and demand sides and at the network level but are more complex to control and manage. In this article, MCESs are reviewed in the context of future low carbon energy systems based on electrification and very high variable renewable energy penetrations. Fully exploiting these systems requires some cost reductions, more sophisticated operations enabled by standardized communications and control capabilities detailed planning paradigms, and addressing their corresponding economic challenges. All these point toward the direction of analysis, markets, and technology research and development coupled with better policy and regulatory frameworks. One futuristic vision of a very low carbon energy system is proposed that illustrates potential pathways to an MCES-dominated energy future.

Demand response (DR) is envisaged to be of significance for enhancing the flexibility of power systems. The distributed nature of residential demand-side resources necessitates the introduction of an aggregator/retailer to represent the flexible demand in the electricity market. However, the objectives of the residential consumers, the retailer, and the system operator might not be aligned. This paper presents a multi-perspective model which integrates the optimization problems of these strategic decision makers within a single framework for evaluation of residential thermal DR. The multi-perspective model is formulated as a bilevel optimization problem and incorporates detailed building state-space models of residential thermal demand. Comparison of the multi-perspective model with existing market-based models shows that it is able to provide a holistic view of residential DR by capturing the interactions in both the retail and wholesale markets. The results also show that centralized optimization models would over-estimate the system value of DR in the presence of strategic market participants. Detailed sensitivity analyses using the multi-perspective model reveal interesting insights on the welfare distribution aspects of DR. The results show that in addition to consumers' flexibility, retail contract design will also play a major role in determining the welfare distribution among the involved entities.

Flexibility is of prime importance for current and future power systems with increasing grid integration of variable renewables. Insufficient flexibility can result in high renewable energy curtailment levels, excessive intra-hour plant cycling and/or frequency stability concerns. Addressing all these issues simultaneously could result in more efficient asset utilization- demand response is one potential option. The challenge, however, is to ensure a suitable adaptation of the flexible demand response to evolving system requirements across multiple operational time scales. To this end, this paper presents a multi-level, multi-time scale methodology for simultaneous provision of multiple system services to alleviate the aforementioned issues using a single demand-side resource. Based on effective aggregation/disaggregation, the proposed methodology is implemented for residential thermal energy storage using different temporal and control levels, while importantly, still guaranteeing satisfaction of thermal comfort requirements of individual dwellings. Assessment of the proposed methodology on a test system reveals that it can significantly improve the system performance at different temporal levels by bridging the gap between individual load control and system-wide flexibility requirement.

Thermal storage capability of residential heating loads can enhance power system flexibility and can potentially facilitate grid integration of variable renewable generation. This paper presents a detailed assessment of the wind integration potential of both active and passive residential thermal storage using the Building-to-Grid (B2G) model. The B2G model integrates buildings’ thermal dynamics and end-use constraints within a reserve-constrained unit commitment tool. The presented case studies evaluate the impact of various factors on the wind integration potential of residential thermal storage. These factors include storage capability of active and passive thermal storage, wind penetration levels, and participation of heating loads in various categories of system reserve. The results depict that utilisation of residential thermal storage can facilitate wind curtailment reduction. However, analysis of the results highlights that the curtailment reduction potential of thermal storage is constrained during large wind curtailment events. The start-up costs reduction potential of thermal storage becomes more important with increasing wind penetration levels. It was also observed that for the considered test system, participation of heating loads in over-frequency reserves is more important than under-frequency reserves in terms of wind curtailment reduction.

Demand response (DR) is envisaged to be of significance for enhancing the flexibility of power systems. The distributed nature of demand-side resources necessitates the need of an aggregator to represent the flexible demand in the electricity market. This paper presents a bilevel optimization model considering the optimal operation of a strategic aggregator in a day-ahead electricity market. Additionally, consumers' requirements in terms of comfort satisfaction and cost reduction are considered by integrating detailed demand models and retail contract constraints. The results on the considered test system reveal that centralized optimization models would tend to over-estimate the capabilities of DR in an electricity market with strategic participants. Also, the flexibility value of DR for the power system and the profitability of the aggregator are significantly dependent on the retail contracts between the aggregator and the consumers, highlighting the need for careful contract design.

Demand Side Management (DSM) using Thermal Electric Storage (TES) presents a promising opportunity for enhancing the system flexibility, resulting in reliable and economic operation of future low-carbon power systems. System-wide analysis of the flexibility potential of TES necessitates representation of dynamic thermal models in large-scale power systems models. Therefore, this study presents a novel Building-to-Grid (B2G) model integrating buildings' thermal dynamics and end-use constraints with a security-constrained unit commitment model for energy and reserve scheduling. The behaviour of residential thermal demand is represented through linear state space (RC-equivalent) models for different residential archetypes. The B2G model is subsequently used to evaluate the energy arbitrage and reserve provision potential of TES for a test system and various sensitivity analyses for wind penetration levels and presence of other flexibility options have been conducted. The optimisation results highlight the significant value of TES in terms of annual generation cost savings, reserve provision, peak load reduction and utilization of wind energy. The findings also emphasize the importance of co-optimising energy arbitrage and reserve provision from TES devices vis-a-vis system performance and household energy consumption scheduling.

Grid integration of wind energy is a major challenge as grid codes require electricity generation units to schedule their generation ahead of the trading period and to limit the power fluctuations. This paper proposes novel strategies for mitigating the effects of wind intermittency by developing hybrid off-shore wind and marine current turbines. Unlike the random nature of wind, marine currents have slow cyclic variations and are highly predictable. The proposed methods involve optimal sizing strategy for the hybrid system and rely on the resource predictions. Prediction Intervals for wind speed forecasts using Bootstrapped Artificial Neural Networks have been developed and validated. Marine current speeds have been mathematically modeled using the Harmonic Analysis Method. Subsequently, novel power fluctuation mitigation and generation scheduling strategies using minimum energy storage have been designed based on the UK electricity market regulations and the predicted wind and current speeds. The results demonstrate the effectiveness of the proposed methods which ensure successful mitigation of power fluctuations, reliable dispatch scheduling of renewable generation, and significant cost saving potential.