Newton Energy Group (United States)

The escalating risk of global warming, driven by greenhouse gas emissions, necessitates urgent advancements in renewable energy sources. Among these sources, microbial fuel cells stand out as a promising electrochemical technology capable of harnessing energy from diverse wastewater streams. These fuel cells leverage the catalytic activity of living microorganisms to convert the chemical energy present in organic matter into electricity. This study explores the application of microbial fuel cells to recover energy from three distinct waste sources: dairy wastewater, sewage wastewater, and urine. Proton exchange membranes were employed as electrolytes during experimentation. Notably, the open circuit voltage measurements revealed encouraging results. Dairy wastewater yielded an observed voltage of 0.26V, while sewage wastewater exhibited a higher voltage of 0.46V. Surprisingly, urine demonstrated a comparable open circuit voltage of 0.42V. By converting organic matter into electricity through the catalytic activity of microorganisms, microbial fuel cells offer a promising avenue to address the pressing challenges posed by global warming and greenhouse gas emissions.

Cogeneration facilities at commercial and institutional locations are significant emitters carbon dioxide. Many large universities, hospitals and large commercial complexes maintain combined heat and power facilities that are interfaced with wholesale power markets. These facilities both buy and sell electricity in the organized markets while maintaining what is their principle function of provision of thermal energy for heating and cooling. In this paper we provide the theoretical background to calculation of Marginal Emission Rates (MERs), provide an overview of the optimal operation of those facilities, and present the results of a detailed case analysis of the results of a comparison of the MER of an operating cogeneration facility at Cornell University compared with the MER for consumption of electricity at the closest wholesale bus of the New York Independent System Operator (NYISO).

The growing use of natural gas for electricity generation has created significant changes in the loads experienced by pipeline systems used for gas transport. This trend compels a fundamental re-examination of natural gas markets to develop pricing that reflects the physical and engineering capacity of pipeline networks. Recent advances in optimization techniques are promising for enabling pipeline optimization to address these needs. In this paper, we present an adaptive partitioning method in combination with optimality-based bound tightening approaches to strengthen standard convex relaxations for a class of steady-state pipeline system optimization problems, mathematically formulated as mixed-integer nonlinear programs (MINLP). Computation time and solution accuracy for objective functions that maximize compressor efficiency and economic welfare, respectively, on meshed gas networks are evaluated and compared to outcomes obtained using an interior point method. The method rapidly solves such problems to near global-optimum solutions. Efficiency and scalability of the technique makes it promising for extension to large-scale problems of dynamic gas-electric co-optimization.

We derive a price formation mechanism to maximize social welfare for a pipeline network that delivers natural gas from suppliers to consumers. The system is modeled as a metric graph subject to physical balance laws for steady-state hydraulic flow on edges and mass balance at nodes. The pricing mechanism incorporates engineering constraints on local pressures and energy applied by gas compressors. Optimality conditions yield expressions for locational marginal prices for gas (gLMPs) and a decomposition of gLMPs into components corresponding to energy, compression, and two types of congestion. We demonstrate that price and pressure differentials between nodes have the opposite sign, so that price cannot decline in the direction of flow, and prove that the pricing mechanism is revenue adequate. We also present computational examples of congestion pricing for a small test network and a large-scale case study.

Offshore wind provides multiple benefits both in terms of economic savings to consumers and reduction in environmental residuals. The current paper uses a state-of-of-the-art software system, pCloudAnalyticsa#x2122; to evaluate multiple scenarios of the incorporation of offshore wind assets in the New England electric power system ranging in size from 100MW to 1200MW. The analysis is focused on the benefits to consumers -- the price suppression impacts -- of increasing quantities of offshore wind. The analysis provides a detailed evaluation of the locational marginal price (LMP) impact of increasing MW of offshore wind for a single study year, 2015. In addition the analysis tracks and presents the reductions in air emissions that result from incorporation of increased quantities of offshore wind in the New England system.

Transmission topology control (line switching) is currently a manual operator intervention by power system operators implemented on an ad-hoc basis and relying on either the operators' previous experience or on a set of fixed procedures linking congestion with topology changes. While the co-optimization of network topology and generation resources has been shown to provide significant congestion cost reduction, the problem is intractable even for moderate-size systems. Our previous work developed near-optimal and tractable topology control algorithms (TCA) that employ sensitivity information extracted from a standard ED solution for a given starting topology to identify promising switch on or off line candidates. We have also developed shift-factor-based MILP TCAs that are more efficient while being consistent with ED algorithms adopted today by ISOs. This paper summarizes experience with and assessment of these methods on real-size intra-day market data based on three historical one-week periods of the PJM system.

Assuring security of supply (resource adequacy) of a bulk power system is one of the main criteria of system planning. Traditionally, system planners study resource adequacy of the system using such probabilistic criteria as Loss of Load Hours (LOLH), and similar metrics which then lead to the determination of the planning reserve margin requirement. Installed capacity equal to that requirement assures the desired probabilistically measured level of resource adequacy. The paper demonstrates that this approach is not applicable to system with transmission constraints. For transmission constrained systems, the paper provides a formal derivation of locational resource adequacy indicators both for generation and load locations and for transmission connections. These locational indicators are defined in the form of dual variables in the problem of reliability dispatch and developed both for generator locations and for transmission constraints; they provide the foundation for determining locational planning reserve margin requirements and for establishing the need for transmission upgrades from the security of supply perspective. The theory is based on the stochastic minimization of the total cost criterion incorporating the cost of system expansion and the damage caused by the loss of load.