Linking Distributed Optimization Models for Food, Water, and Energy Security Nexus Management

Traditional integrated modeling (IM) is based on developing and aggregating all relevant (sub)models and data into a single integrated linear programming (LP) model. Unfortunately, this approach is not applicable for IM under asymmetric information (ASI), i.e., when “private” information regarding sectoral/regional models is not available, or it cannot be shared by modeling teams (sectoral agencies). The lack of common information about LP submodels makes LP methods inapplicable for integrated LP modeling. The aim of this paper is to develop a new approach to link and optimize distributed sectoral/regional optimization models, providing a means of decentralized cross-sectoral coordination in the situation of ASI. Thus, the linkage methodology enables the investigation of policies in interdependent systems in a “decentralized” fashion. For linkage, the sectoral/regional models do not need recoding or reprogramming. They also do not require additional data harmonization tasks. Instead, they solve their LP submodels independently and in parallel by a specific iterative subgradient algorithm for nonsmooth optimization. The submodels continue to be the same separate LP models. A social planner (regulatory agency) only needs to adjust the joint resource constraints to simple subgradient changes calculated by the algorithm. The approach enables more stable and resilient systems’ performance and resource allocation as compared to the independent policies designed by separate models without accounting for interdependencies. The paper illustrates the application of the methodology to link detailed energy and agricultural production planning models under joint constraints on water and land use

Integrated Management, Security and Robustness

The main goal of this publication is to vulnerability of food, water, and energy security sector toward decisions making. On this stage the project focuses on modeling of interrelations of food security, with energy and water security under existing regional changes and threats

Modeling for managing Food-Energy-Water-Social-Environmental- NEXUS security: Novel systems’ analysis approaches

The aim of the talk is to introduce and discuss novel systems’ analysis models and methodologies, in particular, robust machine learning, being developed within a joint National Academy of Science, Ukraine, and International Institute for Applied Systems Analysis project -- Integrated modeling for robust management of food-energy-water-social-environmental (FEWSE) nexus security and sustainable development [1-2]. These approaches enable science-based support of policies providing coherent strategic coordination and regulations among food, energy, water, social sectors, accounting for complex linkages and differences in spatial and temporal scales between agriculture, energy and water security, potential systemic risks, and new feasible policies at various levels. Thus often, detailed sectoral and regional models have been used to independently plan desirable developments of respective sectors and regions. However, solutions that are optimal for a sub-system may turn out to be infeasible for the entire system. The talk presents new approaches based on the linkage of detailed distributed models of subsystems (e.g., sectoral and regional models) under joint resource constraints thus allowing for truly integrative decision support through optimal and robust solutions across sectors and regions [2-3]. The linkage solution procedure is based on the parallel solving of equivalent nonsmooth optimization model following a simple iterative subgradient algorithm [4-6]. The procedure can be considered as a new robust machine learning algorithm, namely, as a general endogenous reinforced learning problem of how software agents (models) take decisions in order to maximize the cumulative reward (total welfare). Based on novel ideas of systems’ linkage under asymmetric information and other uncertainties, we discuss nested strategic-operational and local-global welfare models which are used in combination with, in general, non-Bayesian probabilistic downscaling procedures for analyzing and managing systemic interdependencies and risks. Quantile-based indicators [7] are used to cope with new type of endogenous risks and extreme events generated by decisions of various stakeholders. For long-term sustainable functioning of FEWE systems, robust policies comprise both interdependent strategic long-term (anticipative, ex-ante) decisions and short-term (adaptive, ex-post) decisions (adjustments). The methodologies and models will be illustrated with relevant case studies

Iterative solution procedure for nonsmooth nondifferentiable stochastic optimization: linking distributed models for food, water, energy security nexus management

Detailed sectorial and regional models have traditionally been used for planning developments of respective sectors and regions. However, solutions that are optimal for a sub-system may turn out to be infeasible for the entire system. In this talk, we discuss a new modelling approach enabling the linkage of detailed distributed models of subsystems under joint resource constraints, uncertainty, systemic risks, and asymmetric information. The approach is based on a Stochastic Quasigradient (SQG) iterative solution procedure for nonsmooth nondierentiable optimization problems. The models are linked in a decentralized fashion via a central planner (central "hub") without requiring the exact information about models’ structure and data, i.e. in the conditions of asymmetric information and uncertainty. The sequential SQG solution procedure organizes an iterative computerized negotiation between sectorial (food, water, energy, environmental) systems (models) representing Intelligent Agents. The convergence of the procedure to the socially optimal solution is based on the results of nondifferentiable optimization providing a new type of machine learning algorithms. The linkage problem can be viewed as a general endogenous reinforced learning problem of how software agents (models) take decisions in order to maximize the "cumulative reward". The approach is illustrated by linking distributed agricultural, water and energy sector models for food-water-energy nexus security

Consistent linkage of distributed food, water, energy, environmental (FWEE) models: perspectives of data and modeling platform for integrated FWEE security NEXUS analysis and planning

In this presentation we discuss methodologies, modeling tools and case studies on linking distributed disciplinary food, water, energy, environmental (FWEE) systems’ models into multi-systems multi-disciplinary integrated models for truly integrated analysis and managing of FWEE security NEXUS. Models’ linkage approaches enable to operationalize the concept of modeling and data platforms for distributed independent models’ “integration” and integrated FWEE security NEXUS management. Local, national and global FWEE security in the presence of climate change and risks of various kinds depend on the consistent coordination between and within the interdependent FWEE systems regarding sustainable resource supply and utilization. Detailed independent sectoral and regional systems’ models are often used to address these challenges. However, the independent approaches overlook the close linkages and feedbacks between and within the systems and, therefore, possible cross-sectoral implications. Critical cross-sectoral FWEE systemic supply-demand imbalances can trigger a disruption in a FWEE systems network. Disruptions and failures can be induced by human decisions in combination with natural shocks. For example, overuse of water in one system, e.g., agricultural, can lead to drying up of wells, decrease of reservoir water level, shortage of water in other systems, e.g., for colling power plants or hydropower production; an extra load in a power grid triggered by a power plant or a transmission line failure can cause cascading failures with catastrophic systemic outages; a hurricane in combination with inappropriate land use management can result in a catastrophic flood and human and economic losses, similar to the induced by Hurricane Katrina. These are examples of systemic risks motivating the development of proper models’ linkage approaches and integrated systems analysis. The linkage algorithms are becoming widely demanded in connection with the need for decentralized planning of distributed systems and technologies emerging in agriculture, water, energy, environmental systems, e.g., distributed precision agriculture technologies; hydro-economic models’ linkages; bio-physical crop modeling; distributed energy production. In this presentation we define and illustrate the two main linkage methodologies: linkage of distributed FWEE optimization models (land use, water, energy systems models); linkage of simulation and optimization models (crop-yield meta-model from EPIC and a land-use GLOBIOM model). Both methodologies are based on iterative sequential stochastic quasigradient (SQG) procedures of, in general, non-smooth nondifferentiable stochastic optimization, which converge to socially optimal solution maximizing an implicit nested nondifferentiable social welfare function. The linkage problem can be viewed as a general endogenous reinforced learning problem. The models act as “agents” that communicate with a “central hub” (a regulator) and take decisions in order to maximize the “cumulative reward". The procedure does not require models to exchange full information about their specifications. The distributed models can operate on distant computers of individual agents and “negotiate” with a central computer of a regional planner through the linkage procedure