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Ahmed S. Alahmed

Publications and source records attributed to Ahmed S. Alahmed.

18 recordsLinked to original sources

Optimal Dispatch of a Hydrogen-Colocated Renewable-Powered Desalination Plant

This paper develops an analytical framework for profit-maximizing dispatch of water, electricity, and green hydrogen in a renewable-powered water desalination plant (WDP) combining thermal and reverse osmosis (RO) desalination. The optimal dispatch reveals that the schedules of the desalination units, electrolyzer, and grid interaction can all be characterized in closed form as functions of renewable generation. At low renewable output, the plant relies on thermal desalination and grid power while maintaining RO desalination and hydrogen production at their minimum setpoints. At high output, it reduces thermal desalination, increases RO desalination, and allocates surplus power and water to hydrogen production. For renewable output within a precomputed intermediate range, the WDP balances energy internally, allocating resources based on the relative marginal values of hydrogen and RO water. Simulations using real solar and plant data show that integrating RO and thermal desalination with renewable generation and hydrogen production achieves the highest daily profit of 75.945 k$, exceeding the best configuration without hydrogen by 15.1%.

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Optimal Scheduling of Electricity and Water in Renewable-Colocated Desalination Plants

We develop a mathematical framework for the optimal scheduling of flexible water desalination plants (WDPs) as hybrid generator-load resources. WDPs integrate thermal generation, membrane-based controllable loads, and renewable energy sources, offering unique operational flexibility for power system operations. They can simultaneously participate in two markets: selling desalinated water to a water utility, and bidirectionally transacting electricity with the grid based on their net electricity demand. We formulate the scheduling decision problem of a profit-maximizing WDP, capturing operational, technological, and market-based coupling between water and electricity flows. The threshold-based structure we derive provides computationally tractable coordination suitable for large-scale deployment, offering operational and economical insights into how thermal and membrane-based desalination colocated with renewables complementarily provide continuous bidirectional flexibility. The thresholds are analytically characterized in near closed form as explicit functions of technology and tariff parameters. We examine how small changes in the exogenous tariff and technology parameters affect the WDP's profit. Extensive simulations illustrate the optimal WDP's operation, profit, and water-electricity exchange, demonstrating significant improvements relative to benchmark algorithms.

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Wholesale Market Participation via Competitive DER Aggregation

We consider the aggregation of distributed energy resources (DERs), such as solar PV, energy storage, and flexible loads, by a profit-seeking aggregator participating directly in the wholesale market under distribution network access constraints. We propose a competitive DER aggregator (DERA) model that directly controls local DERs to maximize its profits, while ensuring each aggregated customer gains a surplus higher than their surplus under the regulated retail tariff. The DERA participates in the wholesale electricity market as virtual storage with optimized generation offers and consumption bids derived from the propoed competitive aggregation model. Also derived are DERA's bid curves for the distribution network access and DERA's profitability when competing with the regulated retail tariff. We show that, with the same distribution network access, the proposed DERA's wholesale market participation achieves the same welfare-maximizing outcome as when its customers participate directly in the wholesale market. Extensive numerical studies compare the proposed DERA with existing methods in terms of customer surplus and DERA profit. We empirically evaluate how many DERAs can survive in the competition at long-run equilibrium, and assess the impacts of DER adoption levels and distribution network access on short-run operations.

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Joint Scheduling of DER under Demand Charges: Structure and Approximation

We study the joint scheduling of behind-the-meter distributed energy resources (DERs), including flexible loads, renewable generation, and battery energy storage systems, under net energy metering tariffs with demand charges. The problem is formulated as a stochastic dynamic program aimed at maximizing expected operational surplus while accounting for renewable generation uncertainty. We analytically characterize the optimal control policy and show that it admits a threshold-based structure. However, due to the strong temporal coupling of the storage and demand charge constraints, the number of conditional branches in the policy scales combinatorially with the scheduling horizon, as it requires a look-ahead over future states. To overcome the high computational complexity in the general formulation, an efficient approximation algorithm is proposed, which searches for the peak demand under a mildly relaxed problem. We show that the algorithm scales linearly with the scheduling horizon. Extensive simulations using two open-source datasets validate the proposed algorithm and compare its performance against different DER control strategies, including a reinforcement learning-based one. Under varying storage and tariff parameters, the results show that the proposed algorithm outperforms various benchmarks in achieving a relatively small solution gap compared to a theoretical upper bound.

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Watts and Drops: Co-Scheduling Power and Water in Desalination Plants

We develop a mathematical framework to jointly schedule water and electricity in a profit-maximizing renewable colocated water desalination plant that integrates both thermal and membrane based technologies. The price-taking desalination plant sells desalinated water to a water utility at a given price and engages in bidirectional electricity transactions with the grid, purchasing or selling power based on its net electricity demand. We show that the optimal scheduling policy depends on the plant's internal renewable generation and follows a simple threshold structure. Under the optimal policy, thermal based water output decreases monotonically with renewable output, while membrane based water output increases monotonically. We characterize the structure and intuition behind the threshold policy and examine key special properties.

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Co-Optimizing Distributed Energy Resources under Demand Charges and Bi-Directional Power Flow

We address the co-optimization of behind-the-meter (BTM) distributed energy resources (DER), including flexible demands, renewable distributed generation (DG), and battery energy storage systems (BESS) under net energy metering (NEM) frameworks with demand charges. We formulate the problem as a stochastic dynamic program that accounts for renewable generation uncertainty and operational surplus maximization. Our theoretical analysis reveals that the optimal policy follows a threshold structure. Finally, we show that even a simple algorithm leveraging this threshold structure performs well in simulation, emphasizing its importance in developing near-optimal algorithms. These findings provide crucial insights for implementing prosumer energy management systems under complex tariff structures.

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A Decentralized Market Mechanism for Energy Communities under Operating Envelopes

We propose an operating envelopes (OEs) aware energy community market mechanism that dynamically charges/rewards its members based on two-part pricing. The OEs are imposed exogenously by a regulated distribution system operator (DSO) on the energy community's revenue meter and is subject to a generalized net energy metering (NEM) tariff design. By formulating the interaction of the community operator and its members as a Stackelberg game, we show that the proposed two-part pricing achieves a Nash equilibrium and maximizes the community's social welfare in a decentralized fashion while ensuring that the community's operation abides by the OEs. The market mechanism conforms with the cost-causation principle and guarantees community members a surplus level no less than their maximum surplus when they autonomously face the DSO. The dynamic and uniform community price is a monotonically decreasing function of the community's aggregate renewable generation. We also analyze the impact of exogenous parameters such as NEM rates and OEs on the value of joining the community. Lastly, through numerical studies, we showcase the community's welfare, and pricing, and compare its members' surplus to customers under the DSO's regime.

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Network-Aware and Welfare-Maximizing Dynamic Pricing for Energy Sharing

The proliferation of behind-the-meter (BTM) distributed energy resources (DER) within the electrical distribution network presents significant supply and demand flexibilities, but also introduces operational challenges such as voltage spikes and reverse power flows. In response, this paper proposes a network-aware dynamic pricing framework tailored for energy-sharing coalitions that aggregate small, but ubiquitous, BTM DER downstream of a distribution system operator's (DSO) revenue meter that adopts a generic net energy metering (NEM) tariff. By formulating a Stackelberg game between the energy-sharing market leader and its prosumers, we show that the dynamic pricing policy induces the prosumers toward a network-safe operation and decentrally maximizes the energy-sharing social welfare. The dynamic pricing mechanism involves a combination of a locational ex-ante dynamic price and an ex-post allocation, both of which are functions of the energy sharing's BTM DER. The ex-post allocation is proportionate to the price differential between the DSO NEM price and the energy-sharing locational price. Simulation results using real DER data and the IEEE 13-bus test systems illustrate the dynamic nature of network-aware pricing at each bus, and its impact on voltage.

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Co-Optimizing Distributed Energy Resources in Linear Complexity under Net Energy Metering

The co-optimization of behind-the-meter distributed energy resources is considered for prosumers under the net energy metering tariff. The distributed energy resources considered include renewable generations, flexible demands, and battery energy storage systems. An energy management system co-optimizes the consumptions and battery storage based on locally available stochastic renewables by solving a stochastic dynamic program that maximizes the expected operation surplus. To circumvent the exponential complexity of the dynamic program solution, we propose a closed-form and linear computation complexity co-optimization algorithm based on a relaxation-projection approach to a constrained stochastic dynamic program. Sufficient conditions for optimality for the proposed solution are obtained. Numerical studies demonstrate orders of magnitude reduction of computation costs and significantly reduced optimality gap.

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Dynamic Net Metering for Energy Communities

We propose a social welfare maximizing market mechanism for an energy community that aggregates individual and community-shared energy resources under a general net energy metering (NEM) policy. Referred to as Dynamic NEM (D-NEM), the proposed mechanism dynamically sets the community NEM prices based on aggregated community resources, including flexible consumption, storage, and renewable generation. D-NEM guarantees a higher benefit to each community member than possible outside the community, and no sub-communities would be better off departing from its parent community. D-NEM aligns each member's incentive with that of the community such that each member maximizing individual surplus under D-NEM results in maximum community social welfare. Empirical studies compare the proposed mechanism with existing benchmarks, demonstrating its welfare benefits, operational characteristics, and responsiveness to NEM rates.

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Resource Sharing in Energy Communities: A Cooperative Game Approach

We analyze the overall benefits of an energy community cooperative game under which distributed energy resources (DER) are shared behind a regulated distribution utility meter under a general net energy metering (NEM) tariff. Two community DER scheduling algorithms are examined. The first is a community with centrally controlled DER, whereas the second is decentralized letting its members schedule their own DER locally. For both communities, we prove that the cooperative game's value function is superadditive, hence the grand coalition achieves the highest welfare. We also prove the balancedness of the cooperative game under the two DER scheduling algorithms, which means that there is a welfare re-distribution scheme that de-incentivizes players from leaving the grand coalition to form smaller ones. Lastly, we present five ex-post and an ex-ante welfare re-distribution mechanisms and evaluate them in simulation, in addition to investigating the performance of various community sizes under the two DER scheduling algorithms.

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Operating-Envelopes-Aware Decentralized Welfare Maximization for Energy Communities

We propose an operating-envelope-aware, prosumer-centric, and efficient energy community that aggregates individual and shared community distributed energy resources and transacts with a regulated distribution system operator (DSO) under a generalized net energy metering tariff design. To ensure safe network operation, the DSO imposes dynamic export and import limits, known as dynamic operating envelopes, on end-users' revenue meters. Given the operating envelopes, we propose an incentive-aligned community pricing mechanism under which the decentralized optimization of community members' benefit implies the optimization of overall community welfare. The proposed pricing mechanism satisfies the cost-causation principle and ensures the stability of the energy community in a coalition game setting. Numerical examples provide insights into the characteristics of the proposed pricing mechanism and quantitative measures of its performance.

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Co-optimizing Behind-The-Meter Resources under Net Metering

We consider the problem of co-optimizing behind-the-meter (BTM) storage and flexible demands with BTM stochastic renewable generation. Under a generalized net energy metering (NEM) policy-NEM X, we show that the optimal co-optimization policy schedules the flexible demands based on a load priority list that defers less prioritized loads to times when the BTM generation is abundant. This gives rise to the notion of a net-zero zone, which we quantify under different distributed energy resources (DER) compositions. We highlight the special case of inflexible demands that results in a storage policy that minimizes the imports and exports from and to the grid. Comparative statics are provided on the optimal co-optimization policy. Simulations using real residential data show the surplus gains of various customers under different DER compositions.

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Achieving Social Optimality for Energy Communities via Dynamic NEM Pricing

We propose a social welfare maximizing mechanism for an energy community that aggregates individual and shared community resources under a general net energy metering (NEM) policy. Referred to as Dynamic NEM, the proposed mechanism adopts the standard NEM tariff model and sets NEM prices dynamically based on the total shared renewables within the community. We show that Dynamic NEM guarantees a higher benefit to each community member than possible outside the community. We further show that Dynamic NEM aligns the individual member's incentive with that of the overall community; each member optimizing individual surplus under Dynamic NEM results in maximum community's social welfare. Dynamic NEM is also shown to satisfy the cost-causation principle. Empirical studies using real data on a hypothetical energy community demonstrate the benefits to community members and grid operators.

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Competitive DER Aggregation for Participation in Wholesale Markets

The problem of the large-scale aggregation of the behind-the-meter demand and generation resources by a distributed-energy-resource aggregator (DERA) is considered. As a profit-seeking wholesale market participant, a DERA maximizes its profit while providing competitive services to its customers with higher consumer/prosumer surpluses than those offered by the distribution utilities or community choice aggregators. A constrained profit maximization program for aggregating behind-the-meter generation and consumption resources is formulated, from which payment functions for the behind-the-meter consumptions and generations are derived. Also obtained are DERA's bid and offer curves for its participation in the wholesale energy market and the optimal schedule of behind-the-meter resources. It is shown that the proposed DERA's aggregation model can achieve market efficiency equivalent to that when its customers participate individually directly in the wholesale market.

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Integrating Distributed Energy Resources: Optimal Prosumer Decisions and Impacts of Net Metering Tariffs

The rapid growth of the behind-the-meter (BTM) distributed generation has led to initiatives to reform the net energy metering (NEM) policies to address pressing concerns of rising electricity bills, fairness of cost allocation, and the long-term growth of distributed energy resources. This article presents an analytical framework for the optimal prosumer consumption decision using an inclusive NEM X tariff model that covers existing and proposed NEM tariff designs. The structure of the optimal consumption policy lends itself to near closed-form optimal solutions suitable for practical energy management systems that are responsive to stochastic BTM generation and dynamic pricing. The short and long-run performance of NEM and feed-in tariffs (FiT) are considered under a sequential rate-setting decision process. Also presented are numerical results that characterize social welfare distributions, cross-subsidies, and long-run solar adoption performance for selected NEM and FiT policy designs.

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On Net Energy Metering X: Optimal Prosumer Decisions, Social Welfare, and Cross-Subsidies

We introduce NEM X, an inclusive retail tariff model that captures features of existing net energy metering (NEM) policies. It is shown that the optimal prosumer decision has three modes: (a) the net-consuming mode where the prosumer consumes more than its behind-the-meter distributed energy resource (DER) production when the DER production is below a predetermined lower threshold, (b) the net-producing mode where the prosumer consumes less than its DER production when the DER production is above a predetermined upper threshold, and (c) the net-zero energy mode where the prosumer's consumption matches to its DER generation when its DER production is between the lower and upper thresholds. Both thresholds are obtained in closed-form. Next, we analyze the regulator's rate-setting process that determines NEM X parameters such as retail/sell rates, fixed charges, and price differentials in time-of-use tariffs' on and off-peak periods. A stochastic Ramsey pricing program that maximizes social welfare subject to the revenue break-even constraint for the regulated utility is formulated. Performance of several NEM X policies is evaluated using real and synthetic data to illuminate impacts of NEM policy designs on social welfare, cross-subsidies of prosumers by consumers, and payback time of DER investments that affect long-run DER adoptions.

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Hybrid Top-Down and Bottom-Up Approach for Investigating Residential Load Compositions and Load Percentages

Load points are one of the most vital parts of power systems. Due to the new load forms and programs introduced in the demand side, the load-serving entities (LSEs) no longer deal with lump loads, but rather with more dynamic, rational and price elastic loads. The high inter-temporal and behavioral variability of the load profile makes it almost impossible for utilities and system operators to expect the demand curve with the needed accuracy. A sound granularity of the load compositions and consumption percentages and patterns throughout the year is essential for avoiding energy losses, designing demand-side management programs, and ensuring proper adjustments of electricity rates. In this paper, a simplistic model that can be followed by system operators to initially understand the customers' consumption pattern and the household load structure is proposed. A top-down approach is combined and matched with a detailed bottom-up one, to extract load compositions and percentages. Real and local load profiles integrated with household statistical data such as device time of use (ToU), number of device units per house, and activities exercised in households are all included in the model. The main results of the paper show the load composition in residential demand and the percentage of such composition under seasonal-based scenarios.

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